year g700198462 640…But don’t always agree

25 February 2022

 

5G deployment is well underway – or so ‘they’ tell us.

The ‘conventional wisdom’ around 5G has been that it would take root at the enterprise level and drive business innovation first, then touch consumers though secondary applications and services (although not everyone agrees with that, as you’ll see below). 5G has been a lightning rod; it’s been over-hyped, met with resistance from the public, come up against spectrum availability and use issues, and slowed down by a pandemic of global proportions and resulting supply chain issues. No wonder 5G still does not feel “real” to many businesses and business owners.

Is 2022 the year when that will change? “Yes!”, say the experts. And, “not so fast” say others.

Could the convergence of 5G with other emerging technologies, such as AI, ML, and AR/VR usher in the next industrial revolution – Industry 5.0 - and be the game-changer that brings applications and use cases in healthcare, manufacturing, and agriculture and mining? It will do that and make strides in connecting the unconnected say most. But, not in agriculture, says another. Will 5G will aid the utility sector, or tax the electric grid? Yes!

We turned to experts from industry, government, and academia around the globe, who volunteer with IEEE Future Networks for their insights and perspectives on what about 5G might become “real” in the year ahead. And, although they aren’t all singing in unison, each of their predictions is based on a solid foundation of professional expertise and experience. Here is what they said:

 

Getting Real with 5G Deployment

DavidWitkowskiDavid Witkowski

IEEE Senior Member
IEEE MTT-S Life Member
Co-Chair, Deployment Working Group, INGR (International Network Generations Roadmap)
Founder & CEO, Oku Solutions LLC

2022 will be the year that 5G enters the market in force, with new 5G radio access networks and 5G network cores coming online—it’s the combination of these two that enables delivery of true 5G service in a configuration known as 5G Standalone or 5G-SA. 

We’ve seen some limited deployment of 5G-SA, including T-Mobile’s low-band and reclaimed mid-band networks, and Verizon’s Ultra-Wideband millimeter-wave network. Low-band networks provide wide-area coverage with limited throughput, and millimeter-wave networks provide high throughput but very limited range. Mid-band 5G delivers the best balance between coverage and throughput.

Carriers have added 5G to their existing mid-band networks, but these use the old 4G cores in a configuration known as 5G Non-standalone or 5G-NSA, and without a 5G core throughput is limited. Starting in 2022, 5G in mid-band will use 5G network cores. 2022 is also the year when carriers will complete the shutdown of their 3G networks, freeing up additional spectrum for 5G-SA networks. 

That said, 5G could face deployment challenges in 2022 and beyond. Public anxiety about 5G, which is relatively new technology and therefore sometimes perceived as threatening, is showing up at the local government level in the form of opposition and appeals. In early 2022, the U.S. Federal Aviation Administration’s efforts to delay the launch of 5G in the C-band generated an international media firestorm. And some new features intended for 5G still need to go through the Third Generation Partnership Project (3GPP) candidate release process before carriers and device makers can adopt them.

Nevertheless, we’ve faced similar challenges with previous generations of cellular technology, and I have confidence that 2022 will be the year when 5G becomes real for many people.

 

The Real Deployment Challenge is Cost, Directly Impacted by Energy Usage

Francesco Carobolante profileFrancesco Carobolante

Energy Efficiency Working Group, INGR
Principal, IoTissimo LLC

As highlighted in the Energy Efficiency chapter of the 2021 edition of the IEEE INGR (International Network Generations Roadmap), the viability of the services enabled by 5G and Beyond (5G&B) is directly tied to energy efficiency, which needs to be addressed across the whole ecosystem due to up-time requirements and reliability of the network. While the main development efforts at the system level have focused on spectral efficiency through the use of massive MIMO, the real challenge in the deployment of 5G&B technology is cost, which is directly impacted by energy usage – not just for CAPEX (equipment and installation), but also for OPEX (maintenance and energy cost).

This is true not only in urban environments, where local regulations constrain size and heat dissipation, but also in other locations: while edge computing increases energy demand, new safety concerns for the applications being promoted are forcing regulators to require extended power back-up capability. To overcome these initial headwinds, the industry is now focused on reducing costs through the use of highly integrated electronics, which will lead to a broader deployment.

One of the key capabilities that 5G brings forward is the ability to connect an enormous number of IoT elements, now forecasted in the trillions, thus enabling broad sensing and control applications, especially in industrial, robotics, agriculture and mobility sectors. Yet, the energy required to power them is often a challenge, since it is too expensive to wire them, and not sustainable to service them with batteries. Wireless power transfer is being touted as a solution to this issue, but it can currently only be utilized in limited scale, when other solutions are not viable, due to the poor efficiency of such an approach (around 1% for typical distances required). Advanced energy harvesting techniques are being developed, which are intrinsically more sustainable and scalable to the large number of devices being envisioned.

 

Energy Efficiency Takes the Front Seat to Start Working for the People

BZ Headshot USE SMALL 7 12 16 1Brian Zahnstecher

Co-chair, Energy Efficiency Working Group, INGR
Principal, PowerRox

In 2022, we hope to observe a paradigm shift from recognition of the need for driving energy efficiency into a more pragmatic reality in which major stakeholders start to put far more resources into actions than words. The work of IEEE Future Networks has greatly facilitated by articulating concepts, terminology, and metrics (i.e. – Power Value Chain, Power Cost Factor, 5G Derate Factor, etc.) to enable a focus on global efficiency for the many stakeholders driving the development of hardware and network deployments in convergence with the utility grids that power them. The exciting advancements in the areas of distributed energy resources (DER), such as renewable microgrids, and grid-scale energy storage, all converging around autonomous energy grid management systems, bring us closer to a utopian, end-to-end network that self-optimizes from the micro to the macro levels.

We are also at another very fascinating crossroads in that union of communication systems and DERs driving the mitigation of the digital divide to grey the lines between pure economic gain and socioeconomic impact. The mutual benefits of investing in those on the less-connected end of the digital divide have been demonstrated, albeit at a smaller scale, but are certainly a promising step in the right direction. Powering previously disenfranchised or underdeveloped communities not only adds active cellular subscriptions and social media users but can optimize the utilization of energy while improving QoL (that is right, Quality of Life, not just Quality of Service or Experience, otherwise known as QoS/QoE). Everyone is a winner!

 

5G Becomes ‘Business as Usual’ in the Utility Sector

James Irvine profileJames Irvine

Co-chair, Community Development Working Group, IEEE Future Networks
University of Strathclyde/Power Networks Demonstration Centre

2022 is a year where 5G will start to move from the realms of the possible into providing real ‘business as usual’ benefits to utility networks. As national deployments ramp up around the world, vendors start to provide private 5G solutions, and spectrum issues are eased through initiative like spectrum sharing and efforts such as the 450 MHz Alliance. Hence, 5G starts to become a viable solution to providing more ‘smarts’ in the distribution network at reasonable costs. Conservative minds in a very traditional industry – ‘if it ain’t broke, don’t fix it!’ - are also being focused by plans to turn off 2G and 3G networks over the next few years.

However, while the technology is there, there are still issues to be addressed, and key amongst these is resilience. Utility networks are used to five nines reliability, and mobile networks aren’t there yet. Power autonomy within the communications network is essential and must be improved. Where there is reasonable backup at the moment, it tends to be for earlier generation voice networks which are going away. Utilities expect communications to continue to work for up to seven days when mains power is lost, but experience from recent storms suggest 10-14 days might be required. Typical 4G sites last 30 minutes without mains power

A second major resilience issue is of the supply chain. There has been consolidation of the equipment supply chain, and use of 5G in Critical National Infrastructure has spurred governments to start defining who is able to provide equipment for certain applications. Combined with a trend to have greater vendor lock-in as the standards become more complex, the result is a squeeze that may still delay deployment.

 

2022 is the Year of Private 5G Networks; More Energy Efficient as Time Goes by

Emil Bjornson ProfileEmil Björnson

IEEE Fellow
Working Group on Energy Efficiency, INGR
Professor of Wireless Communication, KTH Royal Institute of Technology, Sweden

Despite the hype around the emerging 5G enterprise market and its potential of reaching $700 billion by 2030, the initial focus of 5G is on the consumer market. Its data traffic grows by 40% per year and has been the main driver for evolving the network infrastructure in past decades. The Massive MIMO technology that is at the heart of 5G base stations changes the game when it comes to network dimensioning. The traffic capacity of a base station used to be a fixed resource to be divided between the customers. However, with a paradigm shift in network dimensioning, 5G capacity grows with the number of simultaneously active customers. It is like having a party where the cake grows with the number of guests, so that everyone gets (almost) an entire cake!

This paradigm shift is enabled by the highly directional transmissions from 5G base stations, which are adapted to each customer’s physical location. Current 5G networks are rather empty in the sense of only serving 1-2 users per millisecond, but they are fully capable of managing 5-10 users at the time, each being assigned the entire spectral bandwidth. The implication is that 5G networks will become increasingly cost and energy efficient as time goes by. We will get more bits per second while spending nearly the same power on the infrastructure side.

The spare capacity in 5G networks is also the enabler for new enterprise services. 2022 is the year when private 5G networks will appear, created through virtualization in the telecom operators’ 5G networks, so that enterprises can outsource connectivity delivery and start to gain access to premium wireless services for demanding use cases.

 

5G in 2022 … Another Year of More Pain Than Gain

WaterhouseRod Waterhouse

IEEE Future Networks, Tech Focus Editor-in-Chief
CTO, Octane Wireless

Sorry for sounding so melodramatic but all the signs are indicating 2022 will be another year of slowly catching up to over-the-top hype created several years ago. What we will see this year is more and more engineering development and roll out of lower spectrum (less than 6 GHz) 5G equipment and the pushing off the more ‘paradigm shift’ technologies into the ‘beyond’ 5G space. This in and of itself is not a bad thing, for it shows maturity of a system. An appropriate analogy from the recent Australian Tennis Open, would be that it’s time to work on your foot work, court coverage, and high percentage defensive shots rather than your overhead smashes, cross court winners, and aces down the tee. 

Things to watch this year in the 5G world is the rural coverage fight; there seems to be a lot of players in this area, and I believe we will see a few either drop out or consolidate. For example, I haven’t heard too much on High-altitude Platforms recently; a great technology but maybe looking for an application? Smart agriculture is another compelling topic especially given the climate interest, although a lot of the goals of this may be achievable with WiFi (or equivalent) and, so, smart agriculture may not be considered a 5G application in the future.

 

5G Deployment and Security

Ashutosh Dutta 2019Ashutosh Dutta, Ph.D.

Fellow of the IEEE
Co-Chair IEEE Future Networks Initiative
Chief 5G Strategist, Johns Hopkins University Applied Physics Lab

In 2021, deployment of 5G technologies has largely been constrained due to factors including the prevailing pandemic, lack of interoperability and testing among vendor products, limited availability of open-source implementation, the absence of compelling use cases due to lack of accessibility to advanced testbeds, and lack of a compelling emerging application that would help drive the evolution of the network, and, finally, spectrum interference. As well, perceived security threats for 5G networks at various parts of the network including supply chain security have slowed down widespread deployment of 5G networks as operators weigh the risks associated with security threats and delay their deployment until proper security mitigation techniques are developed to take care of the risk. Thus, the gap between standardization and deployment has also widened.

While 5G deployment is still in its infancy, the research community has already started conducting research for the next generation, 6G being the next cellular evolution. Various SDOs and standards bodies around the world including 3GPP, ITU, IEEE Future Networks, the Next G Alliance, TSDSI, 6G Flagship projects, the Horizons project, and various other initiatives in Europe, China, Japan, and Australia have started delving into research to fill the gaps of 5G and augment 5G technologies.

Adversity during the pandemic has also led to the innovation of new use cases and applications that could exploit various 5G technologies and help bridge the Digital Divide. As 2022 unfolds, 5G technologies will not be limited to telecom operators, but will be further exploited to support verticals including first responders, public safety, tactical networks, defense, agriculture, entertainment, eHealth, and smart cities, to name a few. Enterprises will take advantage of 5G enablers, namely Open RAN, Edge Cloud, Software Defined Networking, Network Slicing, Virtualization, Orchestration, and AI/ML to customize their private 5G networks and support variety of applications including Ultra Low Latency, Enhanced Mobile Broadband and Massive Machine Type Communications. Support of heterogeneous access and HETNETS will be a norm than exception. This will lead to the co-existence of private 5G networks and Wi-Fi networks. New techniques will be developed to provide seamless priority services and quality of services as the end user moves between heterogeneous access networks. Operators will continue to take advantage of disaggregation of network functions, virtualization, orchestration, and closed loop automation and will implement Open RAN solutions to make their RAN programmable. The vendor and operator communities will continue to embrace open-source consortiums such as the Linux Foundation, the Open Networking Foundation, OpenAirInterface, O-RAN, OPNFV, and Free5GC among others, giving rise to faster deployment.

While 5G technologies have taken care of many of the security issues in 4G/LTE, there are additional security challenges and opportunities introduced by various 5G enablers that make the network programmable, scalable, and resilient. In order to deploy a secured 5G network, mitigation techniques need to be implemented to address the security issues presented by each of these enablers. Hence, there will be an effort by the security and monitoring companies to develop new tools and controls to mitigate the risks associated with these 5G enablers. These tools will implement AI/ML techniques and federated learning algorithms to devise predictive security solutions for zero trust type networks. Customized security architecture will be designed to support a variety of applications while maintaining a tradeoff between security indicators and key performance indicators. Collaboration among various SDOs, R&D consortiums, government, and academia will act as a catalyst towards research for next generation networks resulting in new standards, roadmaps, testbeds, use cases, and advanced proof-of-concept.

 

5G Security in 2022 - Cyber Resilience, End-to-End Security, Resilience-by-Design

Eman Hammad ProfileEman Hammad, PhD, SMIEEE

Co-Chair, Security Working Group, INGR
Assistant Professor, Texas A&M University – Commerce@RELLIS>

Following major cyber incidents and disruptions in 2021, systems security and resilience will continue to gain more invested focus from vendors, service providers, and end-users, especially commercial clients. With an ever-evolving and changing threat landscape, priority will be given to ensuring cyber resilience, end-to-end security, and resilience-by-design. This is mainly shaped by future networks and connected systems’ evolution to be more complex, adaptive, dynamic, and autonomous.

Cyber resilience and converged IT/OT/IoT operations will become more mature enabling more intelligent protective and active mitigation operation on different system layers. We will see advancements in enhancing cyber-resilience by integration of trusted closed-loop security automation that’s context-aware and considers the composite state of the system, types of applications, and operational and security KPI requirements.

Future networks rely heavily on ML/AI to enable its operation. While cyber risks and threats of ML/AI algorithms are acknowledged and somewhat better understood, specific recommendations and security controls remain lagging and in research phases. More development will be seen in model verification, reliability, and trust models, in addition to data quality measures.

Focus on end-to-end security architectures will increase in response to recent significant cyber risks and incidents targeting the supply chain. Efforts incorporating trust-based frameworks and platforms to verify and authenticate entities within an end-to-end architecture will gain maturity (attestation, zero-trust, Software Bill of Materials), and we could be seeing example reference security architectures piloted. This would be a necessary step to ensure gaps in supply chain security are managed and mitigated. Moreover, the next-generation evolution of smart grids, intelligent transportations, public safety, and others will require strict KPIs striking critical tradeoffs between security, reliability, and operational performance. URLLC use-case implementations outside of private 5G networks will gain some traction with some integrated security controls.

 

Industry 5.0: When Human and Robots Collaborate Towards the Next Industrial Revolution

Halima Elbiaze profileHalima Elbiaze

Technical Program Committee Co-Chair, IEEE Future Networks World Forum
Full professor, Computer Science Department, University of Quebec at Montreal, Canada

Industry 5.0 is a human-centric solution where robots collaborate with humans to enable personalizable autonomous manufacturing. Cobots can take repetitive and labor-intensive tasks while humans focus on perception-driven decision making. Together with IoT, digital twin, and AI, 5G and beyond (5GB) technology will play a pivotal role in enabling Industry 5.0. The 5GB radio access network infrastructure is designed to support a massive and dense number of devices as in the Industry 5.0 use-case where millions of sensors, hardware elements, and robots operate. 5GB is finally finding its killer use-case that includes many applications, along with increasing bandwidth requirements, stringent latency constraint and resource-greedy AI capabilities. Many applications of Industry 5.0 are tailored considering sustainability aspects including environmental, economic, and social sustainability such as intelligent healthcare, supply chain management, smart education, and smart agriculture.

Despite the promising ability of Industry 5.0 to transform several industrial segments, there are challenges that need to be tackled to achieve its full potential. For instance, one inherent characteristic of Industry 5.0 ecosystems, that brings a potential challenge in processing and handling, is data heterogeneity and volume. Furthermore, the stringent latency targeted (2 ms network delay and 1 ms jitter) by industrial control systems has been recognized as computationally prohibitive in large-scale networked systems. Thus, resource allocation needs a paradigm shift to a closely coupled control, computing, communication, and caching infrastructure where all devices and tiers cooperate towards some specific goals. Goal-oriented communication has the potential to alleviate the complexities of networked control systems by recognizing that communication is not an end, but rather a means to achieving some goals of the communicating parties.

 

5G: As Critical to Progress as Electricity or Water

Sujata Tibrewala ProfileSujata Tibrewala

Co-chair, Edge Services Working Group, INGR
Worldwide developer community manager, oneAPI

5G is of one of the most ambitious technology disruptors if implemented well. Its open interfaces are designed to be accessible for inter-platform and cross vendor development, and hence it reduces barriers to entry and opens doors to smaller vendors and regions, which have traditionally remained unconnected. However, all is not roses and there are bottlenecks to its adoption. For example:

  • Use of mm waves and Giga Hz spectrum has not been used before, hence its impact on human health is making people skeptical. 
  • Its requirement to be vendor- and technology-agnostic goes against the interest of major carriers who are also implementers of 5G, since they have historically protected their interests by providing vertically integrated solutions. 

 In conclusion, the state of 5G adoption creates a sense of déjà vu. In the early 2000s, industry built excess network capacity in anticipation of wide-scale e-commerce adoption. We are living in the age of e-commerce eating up brick and mortar stores today, but it lagged the industry prediction by at least 10 years. It is hard to say how long it will take for 5G to become mainstream, but we know it is inevitable, since it will level-set the playing field for everyone on this earth, since today connectivity is as critical to progress as electricity or water.

 

Looking Farther Ahead

This brief look into the coming year are the insights of the volunteers of IEEE Future Networks who in their day jobs work as telecommunications strategists, consultants, advisors, professors, developers, and more, with specific areas of expertise.

To see even farther down the road, many of these volunteers lead Working Groups contributing to the INGR, a living document creating a predictive model for communication networks looking as much as a decade into the future. Made up of 14 chapters focused on issues across the plane of establishing, deploying, and applying emerging telecoms networking generations, the INGR:

  • Anticipates applications evolving from converged network generations and emerging technologies.
  • Detects inflection points that may arise.
  • Serves as both an investigator and aggregator of challenges and solutions.

The INGR is a resource that serves as a unified understanding of where future communication networks are headed. Updated versions of the INGR are released annually. INGR chapter overviews are available at no cost and for those who sign up for the IEEE Future Networks Technical Community full roadmap chapters may be read in their entirety.

 

2021 istockphoto19 January 2021

Forecasting what might come about over the course of the next 12 months in a space as diverse and dynamic as 5G would be challenging at any given moment. It seems it would have come to prove downright futile ahead of the year in which COVID-19 dominated practically every aspect of life around the globe.

And, yet, looking back on the 5G forecasts of IEEE Future Networks subject-matter experts from January 2020, the “predictions validated fairly well despite COVID-19 emerging as a major socio-economic disruptor less than two months after we published the article,” reflected David Witkowski, Chair, Deployment Working Group, International Network Generations Roadmap, IEEE Future Networks, and Founder and Chief Executive Officer, Oku Solutions LLC.

Added Brian Zahnstecher, Chair, Energy Efficiency Working Group, International Network Generations Roadmap, IEEE Future Networks, and Principal, PowerRox: “If anything, then I feel COVID-19 has greatly accelerated the exponential increase in network traffic/demand even beyond the pre-COVID-19 predictions. This means pretty much all of the concerns expressed for 2020 are that much more salient.”

Again for 2021, we return to experts from across IEEE Future Networks for insights and perspectives on what the year ahead might hold for 5G.


Realization of 5G Value Through Global Pandemic ResponseDavidWitkowski
David Witkowski, Chair, Deployment Working Group, International Network Generations Roadmap, IEEE Future Networks, and Founder and CEO, Oku Solutions LLC

Dramatic socio-economic effects from efforts to contain the pandemic and reduce COVID-19 infections have manifested impacts on telecommunications in both positive and negative ways.

Citizens Broadband Radio Service (CBRS)—sometimes called “Private LTE” or “Private 5G”—as an in-building technology will be delayed until people again can start occupying buildings en masse. However, CBRS has proven useful in outdoor deployments for community broadband, and we expect this to continue in 2021.

Work-from-home, distance learning, and a shift to telehealth emerged as tools to contain pandemic spread, resulting in dramatic shifts in cellular voice and data usage patterns. Students without home broadband, or with inadequate home broadband, were given hotspots for school use, but, without good 4G coverage, hotspots are not useful. The increased density of simultaneous users during work/school hours in housing areas placed huge stresses on the 4G network, which resulted in many cities asking carriers and operators to speed up their 4G and 5G deployment efforts, a trend we expect will continue in 2021. In some cases, these users will be served by private networks using CBRS, Wi-Fi, and other unlicensed technologies.

The need for pandemic response tools that minimize human interactions creates an increased need for augmented and/or virtual reality (AR and/or VR) technologies. Likewise, the pandemic drives a need for Industrial Internet of Things (IIoT) (e.g., automation systems that reduce the amount of time a person needs to be in close quarters with other people). Both are enabled by 5G’s ultra-reliable low-latency communication (URLLC) and massive machine-type-communications (MMTC) capabilities, so we predict strong interest in finalizing those in technical standards.

In general, we predict that the value of 5G will be realized not only in smartphone and device connectivity but also in the application of technologies to address challenges we face as the world continues to adapt to the pandemic.


A More Serious Call to Action on Energy-Efficient OptimizationBZ Headshot USE SMALL 7 12 16 1
Brian Zahnstecher, Co-chair, Energy Efficiency Working Group, International Network Generations Roadmap, IEEE Future Networks, and Principal, PowerRox

2021 will be the year we stop paying lip service to energy-efficient optimization and start taking it more seriously as a critical call to action because there may be no path forward otherwise. Our Energy Efficiency Working Group (EE WG) has greatly expanded upon the original risk factor of The 5G Energy Gap (5GEG) and generated complementary concepts to help isolate and address related impacts to economics (i.e., The 5G Economic Gap) and even tie to socio-economic factors (i.e., The 5G Equality Gap). More importantly, these concepts have morphed into metrics for assessment and given way to an entire framework for modeling, simulating, and assessing complex chains (also known as “Power Value Chains” or PVC). The key metric of 5G Derate Factor, or 5GDF, is the culmination of all aforementioned factors to simplify into an overall, network metric to be reported and optimized by the framework, which we refer to as the “5G Systems of Systems” (SoS). Any stakeholder can convert their network area of focus PVC into our “universal currency” of energy and chain black boxes together to perform both static and dynamic analyses. The static analysis is necessary to assess a given configuration and identify energy bottlenecks. The dynamic analysis provisions for dynamic optimization of EE for the system as a whole and takes desired operating/financial performance targets to determine a 5GDF and provide recommendations for how to maximize that value for optimal EE performance and, therefore, energy utilization.


Strong Rollout and Penetration of Lower-spectrum 5G Network GloballyWaterhouse
Rod Waterhouse, Co-chair, Publications Working Group, IEEE Future Networks, and CTO, Octane Wireless

Obviously, no one in 2019 could have predicted the huge curve ball that COVID-19 would throw in 2020. To the credit of the many people working in 5G technology-related areas, there was still substantial progress made in the 5G sector—albeit probably not as much as one had hoped to see. One impact of the virus was the strong push for the realization of virtual medical care.

So, my predictions for 2021? Really much of the same that I predicted last year (I know this sounds repetitive), with a strong emphasis on the rollout and, therefore, penetration of the lower-spectrum (less than 6 GHz) 5G network throughout the world. I believe the application areas of interests that I mentioned last year are still going to be relevant and intriguing to watch as 2021 unfolds, with the addition of what role high-altitude platforms (HAPs) may play in our future networks. 2021 promises to be an exciting year for 5G.


Expedited 5G Rollout to Support Applications Driven by Pandemic NeedsWaterhouse
Ashutosh Dutta, Co-chair, IEEE Future Networks, and Senior Scientist, JHU Applied Physics Laboratory, Chief 5G Strategist and ECE EP Chair

The aftermath of the pandemic will be a key factor in determining a surge in various new types of activities around the world in 2021. These include virtual meetings, virtual conferences, remote education, online shopping, telehealth, and streaming, among others. Hence, there will be development of a series of new emerging applications to support these activities. These activities will result in a surge of both signaling and data traffic in the network. This surge in traffic from billions of end devices will result in a potential security risk for both data in transit and data at rest. In order to support the surge in traffic resulting from these applications, it is important to design networks that are flexible and resilient and will be able to scale out and scale down on demand. At the same time, it is important to have proper security controls in place to detect and mitigate various types of denial-of-service attacks. As a consequence, operators will need to augment their networks with various 5G enablers such as software defined networking (SDN), network function virtualization (NFV), edge cloud, cloud radio access network (RAN), and network slicing. Closed loop automation and orchestration will play a central role to make the network more resilient to support the surge in traffic. Closed loop orchestration in conjunction with security function virtualization and dynamic service chaining will be deployed to take care of denial-of-service type attacks in the network. Artificial intelligence (AI)/machine learning (ML) will play a big role to enable predictive security and stop the zero-day type attacks. AI/ML will also play a crucial role in placement of these control loops in the network. In order to make RAN more scalable and flexible, open RAN solutions will be deployed. There also will be a push toward private 5G networks. Thus, 5G deployment will be expedited across the world in order to support the applications caused by the pandemic.


Pervasive Connectivity Fabrics to Support Transformations Driven by COVID-19Kaniz Mahdi Profile
Kaniz Mahdi, founding co-chair, Systems Optimization Working Group, International Network Generations Roadmap, IEEE Future Networks, and Vice President of Advanced Technologies, VMware

  • 2020 was anticipated to be a transformational year for telco, with 5G expected to revolutionize the way we live, work, and entertain. It turned out to be transformational, indeed—COVID-19 being the key forcing function. COVID-19 will continue to dominate the scene through the first half of 2021, but behavioral transformations led by COVID-19 are here to stay, e.g., talent reimagined with distributed workforce, school reimagined with customized curriculum, and entertainment re-imagined with home-theatres, drive-in theatres, and e-sports taking a permanent place in our lives.
  • We can expect expedited 5G deployments, enabling pervasive connectivity fabrics to underpin sustainable distributed operation of the transformations led by COVID-19 and previously outlined.
  • We can expect the rise of edge computing driven by the behavioral transformations drastically reshaping consumer traffic patterns, as well as a high degree of process automation required to sustain such operations, resulting in “elephant flows” too costly to carry over long-haul networks.
  • Extended reality (XR)—AR/VR/mixed reality (MR)—finally will become mainstream, as an essential user interface (UI) tool to enhance user experiences driven by sustainable COVID-19 transformations.
  • AI will continue to dominate the research scene, with larger emphasis on brain machine interfaces, another manifestation of sustainable COVID-19 transformations.
  • Bots everywhere—from consumer homes to manufacturing plants to shopping malls to healthcare facilities.
  • Cloud providers will continue to extend their private backbones, capturing the bulk of the traffic shares getting closer to the user (e.g., Google Stadia).
  • We can expect the advent of non-terrestrial networks (low-orbit satellite systems such as SpaceX Starlink) initially to bridge the digital divide, as well as continued tech advancements toward adoption with the autonomous-vehicle industry.

Cell-free Massive MIMO as Research Focus and Major System Architecture ChangeWebert Montlouis Profile
Webert Montlouis, Co-chair, Massive MIMO Working Group, International Network Generations Roadmap, IEEE Future Networks, and Chief Scientist, Applied Physics Lab, Faculty, ECE, Johns Hopkins University

Cell-free massive massive-input, massive-output (MIMO) will see a lot of activities in the research community. The cell-free massive MIMO architecture will bring new ideas and ultimately drive innovation in other parts of the system, which will have significant importance for 5G and beyond. Although the approach can be seen as user-centric, it is expected to bring considerable improvements to the wireless network infrastructure. This architecture will be a driving force to meet the needs of the IoT in years to come. The cell-free concept will evolve into a major system architecture change that will drive a system partitioning and ultimately affect the edge and cloud computing. Some of the key areas will be:

  • Spectral efficiency.
  • Beamforming.
  • Power-optimization techniques.
  • Resource allocation.
  • System partitioning in a cell-free massive MIMO architecture.
  • Edge-computing architecture and cell-free massive MIMO.
  • Cloud computing and cell-free massive MIMO.
  • Load balancing to achieve energy efficiency and maintain latency requirements as the number of antenna elements increases.

Boundary-breaking Dialogue Among Electromagnetic-Spectrum StakeholdersAlex Wyglinski Profile
Alexander Wyglinski, Co-chair, Community Development Working Group, IEEE Future Networks, and Professor of Electrical Engineering and Robotics Engineering, Electrical & Computer Engineering, Worcester Polytechnic Institute

  • During the next 12 months, expect some exciting and important dialogue between two major electromagnetic-spectrum stakeholders who do not normally communicate with each other: the emerging wireless-technologies community (e.g., 5G/6G and Wi-Fi 6) and the radio-science community (e.g., radio astronomy, GEO remote sensing, and radar). With the spectrum landscape experiencing significant flux—and as new wireless technologies progressively utilize frequency bands located close to spectrum allocated to radio science applications—these discussions are a necessity, in order to mitigate radio frequency interference (RFI) and enable sustainable spectral coexistence.
  • The COVID-19 pandemic has significantly accelerated society’s move to an online format, including education, healthcare, and employment. This online way of life requires reliable broadband connectivity, and this pandemic has exposed the large digital divide that currently exists around the world between those who possess enough bandwidth to support their needs and those communities who are underserved or unserved. What we will see in 2021 is a significant effort across government, industry, and academia to bridge that digital divide, by developing broadband connectivity solutions that can reach these underserved/unserved communities, which are mostly located in rural areas with limited network infrastructure. Although part of this effort will focus on rolling out fiber to these communities, there also is an exciting opportunity to employ 5G technology tailored to the specific operating environment to achieve reliable broadband connectivity. However, bridging the digital divide cannot be achieved solely with technology. It also will require the help of other stakeholders, such as utility companies, community leaders, politicians and regional government, and economists. This is very much an interdisciplinary problem, but 5G technology is a game changer.
  • 5G security and privacy will continue to be a major topic for the communications sector in 2021. Given how quickly 5G is being rolled out and assuming an ever-important role in supporting our society’s information needs, there is growing concern these very complex networks continue to be vulnerable to cyberattacks. We will see large-scale investment in activities related to securing the 5G network from threats introduced via a compromised supply chain, as well as the incorporation of untrusted hardware into the 5G infrastructure. Additionally, as 5G technology gets blended into the existing telecommunications infrastructure consisting of 4G and 3G cellular networks, the intersections where these technologies meet will be prime targets of attackers. Although the identification of these vulnerabilities can really be performed using actual 5G hardware, 2021 is experiencing significant activity in the construction of realistic 5G testbeds to examine this technology more closely, understand its behavior, determine threats, and provide a basis for the development of solutions to harden this increasingly vital infrastructure.

IEEE Future Networks invites participants in all aspects of current and future connectivity globally into collaboration on enabling the historic transformation promised by 5G and beyond for the benefit of all. Learn more about IEEE Future Networks.


cyber securityIEEE Future Networks Podcasts with the Experts

An IEEE Future Directions Digital Studios Production 

5G Security, Part I: Foundational Security Capabilities

In this episode, we bring you Part I of a two-part podcast on 5G Security. In Part I, our experts discuss foundational security capabilities, and in Part II the focus is on use-cases, including device and application security.

The digital transformation brought about by 5G is redefining current models of end-to-end (E2E) connectivity and service reliability to include security-by-design principles. These principles are necessary to enable 5G to achieve its promise. Achieving 5G trustworthiness necessitates the importance of embedding security capabilities from the very beginning while 5G architecture is being defined and standardized. Security requirements need to overlay and permeate through the physical, network, and application systems layers of 5G, as well as different parts of an E2E 5G architecture, including a risk management framework that takes into account the evolving security threats landscape. The Security Working Group within the International Network Generations Roadmap follows a taxonomic structure, differentiation 5G functional pillars, and corresponding cybersecurity risks.

View the International Networks Generations Roadmap page with Executive Summary, and options for viewing the Security Chapter.

Subject Matter Experts

ashutosh dutta


Ashutosh Dutta 
Co-chair, Security Working Group, International Network Generations Roadmap.
Co-chair, IEEE Future Networks Initiative
Senior Scientist, Johns Hopkins University Applied Physics Lab.

 

 

eman hammad

Eman Hammad
Co-chair, Security Working Group, International Network Generations Roadmap
Industrial and IoT Security Specialist in Cybersecurity & Privacy, PwC Canada

 

 

With Brian Walker of IEEE Future Directions Digital Studio

Click here to listen. 
Click here to download. 

Subscribe to our feed on iTunesGoogle Play, or Spotify

Podcast Transcript 

Brian Walker: Can you tell us about 5G Security and why this generation presents a unique set of risks?

Ashutosh Dutta: 5G is different than previous generations in many ways. The previous generations can be categorized as 1G, 2G, 3G and 4G. Previous generations of network, they mostly focused on bandwidth intensive application. On the other hand, 5G networks not only focus on high-bandwidth type applications, but also focus on massive sensing and ultra-low-latency type applications. So, in order to support that, there is a need to design into an architecture, a network that's flexible, adaptive, and scalable enough to react to the changes in the network quite rapidly and efficiently. And thus, 5G has adopted many new technologies in various parts of the network. That includes changes in the radio access network, such as new radio, MIMO, millimeter wave in the core network, separation of user plane, control plane, service plus architecture, densification of cells, entities like edge-cloud, software-defined networking, network slicing, device-to-device communication, and IoT, etc. So, security requirements for 5G do need to overlay and permeate through different layers of 5G systems, including physical, network, and application, as well as different parts of end-to-end 5G architecture. And that will also include the risk management framework, and take into account the evolving security threat landscape. So, while the enablers bring various benefits and features, they also increase the threat landscape due to the introduction of these new enablers or technologies. For example, the additional SDN/NVF components and network functions, they increase that threat attack surface, and expose the end-to-end system to additional cyber threats. Some of the security risk that are pertaining to 5G may include network slicing threats, such as denial of service to other slices, or sites and other attacks at core slices. And we find, for example, when a certain slice is dedicated to a specific critical set of applications, such as emergency applications. So, all-in-all, the 5G network, although it includes a lot of features and adds some enablers, it also increases the threat landscape by posing unique sets of risks.

Eman Hammad: Just one thing on the last point you mentioned, when 5G evolved to carry more of the critical and more of the cyber physical applications and use cases, such as smart cities, transportation, energy systems, these kind of risks that might hinder the infrastructure will have a serious impact on where 5G is trying to enable the applications, specifically for such use cases. So, we're looking at a very different landscape, because of all the pieces that build the 5G system.

Brian Walker: What are the additional security pillars in 5G ecosystems?

Ashutosh Dutta: When you look at the 5G ecosystem, basically looking at end-to-end, you have a device, you have a radio access network, you have edge-cloud, you have the core network, and you also have applications. And if I look into the different pillars, from an end-to-end perspective, these pillars enable 5G features, right? But at the same time, we're also looking into the risks that are associated with pillars, and I can just name a few. To start with cloud-RAN security, SDN controller security, proactive security analytics, virtualization security or hypervisor container security, orchestration security, network slicing security, Open Source security, security function virtualization and faster authentication. So, these are some of the additional components where we have to look mainly at what are the additional risks, those are provided by adding these new 5G enablers. For example, if you have an SDN controller that gives you the programmability and flexibility, adaptability on the network, but at the same time, there are additional risks on the SDN controller. If the SDN controller is under attack, or this configuration gets changed, or somebody hacks into the SDN controller through a northbound API or southbound API, the whole programmable part becomes completely contaminated. So, instead of stopping bad traffic, it's going to allow the bad traffic to go through. Similarly, if you have orchestration security, if you do not have enough security on the orchestrator that allows to instantiate new network function, VNF, the Virtual Network Functions, one can just highjack the orchestration procedure and instead of orchestrating a specific VNF, it may orchestrate a wrong VNF in a wrong place. And that itself is pretty bad in terms of reliable operation. Similarly, for the hypervisor virtualization security, if a hypervisor is under attack or gets compromised then the VNF, the Virtual Network Function, from one tenant may be able to attack another tenant by compromising the reliable operation. So, these are some of the pillars. So, when we design an end-to-end 5G network, we need to look into, and make sure we have proper security requirements and mitigation techniques, which are available and designed from the very get-go.

Brian Walker: Where are these risks focused? Are they mainly at the consumer level? Or at the enterprise level as well?

Ashutosh Dutta: So, different security risks that are uniquely related to 5G are both on the consumer level and as well as the enterprise level. I'll just give you an example, because 5G's currently positioned to enable connectivity across many verticals. So, the security risk will be largely determined by the types of use cases for these verticals or the types of applications that are supported by these verticals, right? And these verticals could be transportation, first responder networks, smart city, tactical networks, auto industry, and automation. And the applications they support may have different key performance indicators. For example, they may need a different kind of bandwidth, different kind of latency, or different kind of system control. So, based on these KPIs we need to design our 5G network and, for each of these KPIs, we have to enable those components like edge-Cloud or SDN / NFV that may need proper security measures to be in place.

Eman Hammad: Maybe one point to add into this is, it's helpful always to look at the reference architecture for 5G. So, after the edge we're looking into the end users, devices, and systems. Systems that are enabled by 5G. And then after that demarcation point, we're looking at the main core networks and the connectivity to the Cloud. Now, as to end user level, we might be even dealing on that level with enterprise and end users as well. So, that speaks volumes to the complexity of 5G, and how the risks are propagated through even some architecture demarcation points, making it much more challenging to enforce cyber security functions, and require much more creativity and critical thinking when imposing security solutions. For example, one example is in one survey - most of the respondents from IP providers and telecom carriers - responded saying that they are looking at Zero Trust models to enable security in 5G. And Zero Trust models require really robust, scalable certificate management on all end devices and subcomponents of the 5G architecture, within the endpoint ecosystem, and within the 5G core system. So, this is one of the solutions that can actually mitigate risks from end-to-end on the 5G platform, some types of risks.

Brian Walker: And how do you anticipate security risks in mitigation with 5G evolving over time?

Eman Hammad: So, within any security framework, when you look at the complex system, and you look at a system that incorporates, as Ashutosh mentioned, many building blocks as well. The designers of the system have to incorporate from the very beginning an approach to assess the threats that are evolving with time, and assess the impact of such threats as they are actually happening. And this is usually formalized through a risk management framework. So, we believe that this is conducive for a 5G network to perform these realistic risk assessments on some of the scenarios and people should pay attention to the scenarios that are relevant to the use cases. For example, when you're really looking at risks of the type of electromagnetic interference, this has always been there, but with 5G and the different use cases for 5G, this takes a new shape and form. Also, risks related to resources exhaustion, and this is relevant to SDN, virtual function, cloud resources, slicing. So, any of these modular blocks that shape a service, if they are targeted within the control platform, or from a user via a massive IoT risk request or something of that sort, this will cause a resource exhaustion that will impact the reliability of the 5G as a whole system, or a major component of 5G. So, a risk management platform will say, "Okay, these are the risks that are currently happening. Let's assess for some of the use cases: what are the impacts, and based on that, let's look at existing security controls that are implemented, and what's missing? What are the gaps, and what's the priority to mitigate the high risks within that timeframe?" And adopting that methodology will help us as the threats continue to evolve. Because threats are just a point in time, and your security measures are a point in time. Now, a basic evolution or innovation that will help with 5G is incorporating that risk assessment into security automation. Meaning, if by some innovations and research, we can formalize an indicator of risk saying, "Okay, based on the latest threat intelligence, we know this is happening outside-- in the environment, in the ecosystem, then can we automate part of the provisioning of security functions to proactively protect certain parts of the network in anticipation of this threat vector?" So, this is where automation ties into risk management. This is not yet the case, but we expect this to be more prevalent in future systems to enable us to mitigate evolving cyber threats.

Ashutosh Dutta: I'd like to add a few things. I'll just take a specific use case. And we have a case here in which 5G is evolving over time. So, today we have 5G, 5G beyond, maybe it'll go 6G. There is one specific example I'd like to give, which is Open Source. As 5G and beyond evolve we are going to be relying maybe more on Open Source type solutions and while Open Source gives us a lot of modularity and acceleration to deploy, it also has potential risks, so that is something we all need to keep in mind. What are the potential risks associated with Open Source? And the second thing, Eman, you talked about automation, which is very important. How quickly you detect if there is an attack happening and how quickly do you mitigate that? This closed-loop automation, to a large extent, depends on the traffic variance, the service variance, which type of application, and they call it control variance. Where exactly do you want to put this closed-loop automation? Whether it should be in the edge, or should be in the core, or deep down in the application layer. So, to a large extent, that will determine how quickly we can detect and mitigate. And over time, I'd like to add that some of the AI and machine learning techniques could be applicable to determine the attack before the attack actually happens. And Eman, I think you talked about Zero Trust, right, how we can deploy those techniques to prevent the attack altogether.

Eman Hammad: Yeah, excellent point, Ashutosh. Just a follow-up, even when we look at automation that is enabled by A.I. and machine learning, as we have discussed many times, how to monitor A.I. and machine learning to establish the trust in the algorithm and to ensure that it continues to perform consistently with what has been designed, the design goals for the algorithms, right?

Brian Walker: What potential solutions might be developed to make 5G reliably secure?

Eman Hammad: So, when we look at the solutions that will enable 5G to catch up, to continuously be ahead of the game in cyber security and be able to deliver to its promise of being reliable and resilient. Some of the main trends are already being seen now, but they need to be evolved over time and innovated over time. One of the building blocks is encryption and certificate management. And one of the subtleties in that is that quantum computing threatens current encryption algorithms and certificate infrastructure. So, to be robust, future robust, or future proof, we have to start right now looking into PKI infrastructure, public key infrastructure to enable the Zero Trust models with quantum safe algorithms and encryption. That's one of the approaches. The other approach, we are looking now into Zero Trust. But as Zero Trust will require a massive scale of public key infrastructure and certificate management. We have started seeing trust platforms such as blockchain. Blockchain enables you within that certificate management to say, "This endpoint is trusted to actually connect to the network, or this antenna, or this base station is not a rogue space station. It's an authentic part of the network." So, similar solutions to establish trust, whether it's blockchain or something similar, that's scalable and reliable when it adds to reliability of the 5G network. Other solutions to take into account are the use cases for low latency. And the challenge comes from, let's imagine together use case for transportation or power systems. Within, if we are looking at 5G enabling these low latency use cases, then the delay that is incorporated by security functions going back through to the core, going to the core and back, this might not be acceptable within that key performance indicator for such use cases. So, we'll say, "Okay, at the edge, how can I enable these use cases within the low latency requirements with security guarantees?" And this will require innovations into light authentication or fast authentication. And this is beginning to be investigated in the academia and industry as well to enable these use cases. So, as mentioned so far, trust platforms, safe quantum algorithms or encryption, and low latency light authentication, for example. These are three examples of gaps that are existing in security controls right now that need more investment to establish reliable and robust security controls. In addition, one other point that we have to address as well is A.I. and machine learning. We know that the complexity of 5G will require orchestration and optimization that can only be handled by the likes of machine learning and A.I. And this will become more prevalent as we go to 5G-and-beyond networks. Now it’s necessary to establish some monitoring of these algorithms as they function, especially if they are to orchestrate security functions.

Ashutosh Dutta: The dynamic service churning, or security as a service is an important piece, and that can be enabled by having a closed loop that we talked about. Something that detects. That means you need to have a solid platform for security monitoring that means the ability to monitor the user plane, control plane and data plane signaling. At the same time, having analytics that can provide analytics to an orchestrated platform like ONAP (Open Network Automation Platform), and then having an interface from your orchestration platform to a software-defined network controller, like ODL (Open Day Light) or ONOS. And finally having some enforcement point on the DDOS (Distributed Denial of Service) or IDS (Intrusion Detection System) or IPS (Intrusion Prevention System) type functionality that interfaces with this SDN controller. So, that will allow one to detect any attack that's coming up, either at your Cloud-RAN or in the core, or in the application layer. And how quickly you can send it to orchestration. So, what is going to happen is you recover and resolve at the same time. So, while you're detecting the attack, who is attacking it, and the details of the attack, and trying to mitigate, at the same time you are recovering the network resources by having the ability to dynamically scale up the network. So, in that situation, in the case of a denial-of-service attack, you can still sustain the attack and any kind of priority services there will still continue to be provided while you figure out who is attacking it, and finally mitigate that, and then scale down the network. So, I believe this kind of security-as-a-service with the help of dynamic servicing will be very, very essential.

Eman Hammad: It speaks to the opportunities that are provided by 5G for isolation, proper mitigation, and forensics afterwards. I thought maybe we should add something on the application layer for our security capabilities because that will enable 5G to continue to operate. Because as you know 5G extends from the physical layer to application layer. One of the use cases for security is around 5G fraud. And we haven't touched on that previously. But within 5G fraud, with 5G, with all the pieces that tie into it has enabled—has better enabled providers and carriers better control over fraud within the subsystem and within the use cases.

Brian Walker: As threat vectors targeting 5G evolve, what would be the best approach to ensure reliable system operation?

Ashutosh Dutta: That means we need to have a closed connection between the KPIs that you discussed, for any specific type of application, and then how do we adapt your network accordingly to make sure your KPIs are properly attained. The other thing I was thinking is, they call it defense-in-depth. That means you have to design your network, and from the very get-go, you need to keep all the security threats and potential security risks in mind, and design your network accordingly. It is not like you wake up and figure out, "Well, this is a security risk, and I got to re-design my network." So, we have to keep in mind potential threat vectors, and do an end-to-end analysis. When I say that, you open up your network, open your network interface, look at all the components that comprise your network, and try to do a thorough analysis, we call it threat taxonomy, of each of the components, each interface, and what types of attacks could happen. If you design that ahead of time, then you can think about the mitigation techniques, and potential risks mentioned earlier, to have the mitigation technique that can be applied. If you do not have a security control, you make sure you put the proper security control in place. It has to be like an iterative process.

Eman Hammad: Yeah, yes, exactly. And that's what a risk management framework does. But I like the point, Ashutosh, where you mentioned the system is a not a point in time. The system design is not a point in time. So 5G is an evolving, living system, and I don't think that's an overstatement, because the orchestration, the automation, the optimization, the dynamic allocation of resources between the user and the control plane, the different -- how you can actually shape, and it was based on the changing KPIs, based on the changing attacks, based on the current state of system security or system service levels, required service levels, attest to the requirement for operators, security analysts that are working on the system, and automation like what are device levels of automation to make that feasible. It goes back into, yes, you have to have a very deep understanding of the system, the services, the APIs, the system calls between the APIs and what components work, how. And even in the design of network slicing for proper isolation, so that you have a reliability metric of the system because of the Security by Design that you have done at the very beginning that you will continue to do over time. And use cases change as the threat landscape changes, and as your load, or your requirements change.

Brian Walker: What are the inherent security opportunities resulting from 5G enablers?

Ashutosh Dutta: So, first of all, I'd like to make it clear when you say security opportunities, this means when we add or move to a new type of network, or new enablers, that provides us some opportunity and reduces the CAPEX or OPEX and helps smooth operation of the network, etcetera. But we already discussed some of the security issues associated with those enablers. What I'm talking about here is there are some security implications that could be minimized by having the enablers. So, there are two things. One is by introducing the enablers that are security issues that need to be taken care of. And this question we're talking about, the security opportunities that will be provided by these enablers, which are otherwise not available in previous generation of networks. I'll give you a few examples here. The first example is by having a 5G network, and 5G technology, inherently gives the resiliency support and flexibility support. So, I'll just take the example of network function virtualization, - NFV / SDN - these are the enablers of 5G. When they were not there, if there was an attack taking place, a denial-of-service attack, and, at that time it was taking a long time to detect, a long time to re-provision the network, or dynamically provision the network, but with 5G technology, this specific issue is taken care of because NFV and SDN, by default inherently provide this resiliency of the network by scaling up and scaling down the network on demand. So, the effect of a denial-of-service attack is minimized by having this resiliency. At the same time, if there is attack taking place, how quickly can we detect and mitigate that? By having an SDN controller in this closed loop function, it has the ability to dynamically service chain the DDOS, IDS and IPS functionalities and block this malicious traffic from going to any customer's premises. So, that is an opportunity, the programmability and flexibility. The third one I wanted to talk about is slicing. Network slicing is another enabler. So, network slicing itself provides the ability to assign resources to priority applications. You know, let's say an IoT application, first responder application, automotive application, they all get resources reserved from end-to-end. So, this is an opportunity, right? Well, at the same time we discussed earlier, slicing itself also gives rise to some security issues that needed to be taken care of. So, we are just highlighting some of the opportunities, security opportunities, those are provided, because if there is no slicing, the priority services’ quality of service is not properly maintained, and you cannot separate one application from another application, right? If the first responder wants to send a high quality of service, audio and video, that can be made possible by the use of slices. But, at the same time, virtualization is another opportunity where you can segment your computing resources and provide one type of application to one VNF or tenants, another type of application, another VNF with tenant, right? So, these are some of the high-level security-related opportunities that we can obtain from 5G type technology.

Eman Hammad: I just want to follow up by saying these capabilities of 5G, the enablers of 5G do provide some inherent characteristics that are like intuitive to expand our security and controls. Even provide security controls that were not available in other systems, such as slicing. What I wanted to add is to make sure that we reap the benefits of these inherent characteristics, we need to actually implement additional security controls and properly design the system with this thinking in mind. Meaning, for example, when we design the slicing, we have proper isolation of the slices, and some cap on resources for critical slices, for example. Critical services are assigned slices to make sure that if something happens to the resources, these slices continue to survive. This takes us to talk about other types of opportunities that come from 5G, and I guess I hinted at that when we said, "properly designed with security in mind." So, this requires investment in the security of 5G. And a continuous improvement of the security, but this could give us several folds of benefits. One benefit is to enable our system being resilient, flexible, reliable, as Ashutosh mentioned. The other thing is it enables the providers and carriers to extend their business into new elements. And I'll mention one example. We hear more about security-as-a-service. If a service provider or a carrier implements the proper security controls up to the edge, with monitoring, with some forensics and incident response, then that service provider can actually offer security-as-a-service for its clients, including enterprise clients, and different use cases such as certain transportation providers or a smart city.

Ashutosh Dutta: I’d also like to add one more thing I forgot to discuss, which is very, very important. As the operators are trying to get into 5G, they're building this eMBB (enhanced Mobile Broad Band) type network, one of the components is Cloud-RAN, and separation of user plane and control plane, right? So, by having Cloud-RAN or Open RAN type networks, you are separating your BBU (Base Band Unit)and RRH (Remote Radio Head) functionality, so thereby you are also dynamically adding RAN functionality, RAN functions in the networks in case of lots of IoT type devices, millions of IoT devices trying to get connected. You can easily scale up your RAN functionality on the BBU side in the Cloud right? And the security monitoring mitigation technique can be easily ported in Cloud-RAN, thereby, you can detect the type of attacks early enough, so your core network is not really affected, so that is another opportunity as well.
Brian Walker: Where can people learn more about 5G security?

Ashutosh Dutta: Eman and I are the Security Working Group Co-Chairs, that's one of the Roadmap Working Groups for the IEEE Future Networks International Network Generation Roadmap, we call it INGR. There are 15 roadmap working groups, and Security is one of them. If you visit the website, futurenetworks.ieee.org/roadmap, you will get to know more details. We have already published the first edition of the INGR Roadmap. You should be able to find out on the website some of the things Eman and I spoke about today, but it goes into details of three-years, five-years, and ten-years, what the security landscape is going to look like. How's it going to be useful for 5G-and-beyond, 6G or 7G. So, that's a good source you can take a look. In addition, we also have bi-weekly meetings, you're welcome to join this group, contribute. We have started working on the second generation of the Roadmap. At the same time, there are lots of opportunities like podcasts like we are doing today, webinars, research articles and testbeds, etcetera.

Eman Hammad: I want to add just one thing, which is that being involved with this initiative within IEEE enables you to kind of get more visibility into what's happening in parallel with the different initiatives and efforts, as well as help shape, if we feel, for example, there is a need or a serious gap in security in a certain area that we anticipate to be prevalent in five to ten years, we can point into that, and we can actually work on making special editions, special journals, special conferences, symposiums that will provide an incentive for people to pay attention to these gaps. So, it's really worthwhile to be part of the ecosystem.

Ashutosh Dutta: Yeah, that's a good point. So, there are lots of resources here, there are 46 Societies within IEEE. Over 23 of those are contributing to IEEE's Future Networks Initiative. And the Security Working Group also does collaborate with the other 14 Working Groups to see what are the potential security implications they might have. For example, MIMO, hardware, millimeter wave, edge automation applications. So, we have an opportunity to interact and collaborate with them and find out how we can look at their work and what are the potential security implications there. So, this is happening within IEEE, but we also do collaborate and attend 11 other standards groups like 3GPP, IETF, ITU and ETSI. And we try to complement the work they're doing by developing new technologies, or new algorithms, and new optimizers and techniques in security, how it can help the architecture being developed by, let's say, 3GPP, or protocol developed by IETF. So, that is a real benefit. Anything, Eman, on the standards' side?

Eman Hammad: Just the collaboration with the Standards Group of IEEE, because we all know how the strength of IEEE when it comes to standards. So, there's also the opportunity within IEEE to look at the gaps in standards, or try to facilitate more conversations between the main standards for these that you mentioned, Ashutosh.

Ashutosh Dutta: Right, and the other thing I wanted to also mention are the testbeds. Within the IEEE Future Network Initiative, we have a Testbed Working Group. But at the same time, we also have an MOU agreement with a few of the testbed like RUTGERS/WINLABand 5G Lab in Germany, and a few other testbeds where IEEE volunteers or members of Future Networks get a chance to login and do any kind of experimental work. So, for example, if somebody wants to try some security related experiment, they can join this group, and by being a member of this group, you get access to these labs, and can build your own experiments. At the same, IEEE standards activity recently has come up with IEEE Open, where they're building an Open Source testbed. Thereby, you can try various security related experiments. So, collaboration is very important, not only with academics, with industry, with the vendor community, develop the security requirements ahead of time, bring it to standards and build some proof of concept to make sure some of the security challenges or issues that we talked about should be validated or demonstrated, right? So, it's like a complete ecosystem. And we need help from, and collaboration from everybody around the world to make 5G and beyond more secure.

Eman Hammad: One final thought is around trust. So, when we discuss security or cyber security in general, we're talking about how we trust the technology that enables our day-to-day life. And this is exactly what we're talking about when we talk about being involved in shaping the initiatives; or try to be part of the ecosystem, to make sure that we build trustworthy systems, or we help guide the design and build out of trustworthy systems.

 

digitization 4689530 1920

Predictions on the 5G Ecosystem from the IEEE Future Networks Initiative
14 January 2020

2019 was the relative calm before the 2020 5G deployment storm. Carriers took their first steps in network deployments while engineers worked through gnarly technical challenges, and important tests of the technology were performed in real-world conditions.

It became clear in 2019 that 5G was much more than an upgrade to our mobile phones, and that its first use would not be consumer led. Rather, for the first time in any generation of wireless, enterprise applications would lead the way as first adopter.

The IEEE Future Networks International Network Generations Roadmap (INGR), First Edition predicts, “..it is anticipated that 2020 will see a transformation of the communication industry as multiple new (and powerful) players will fight for market share in which content and ease of use will be the driving factors.”

 We tapped into IEEE Future Networks Initiative subject matter experts, many of whom are involved in the INGR, to get their insights, perspectives and expert opinions on what is to come in networks in 2020. This is what they said:


Spectrum MatterstimLee
Timothy Lee, Co-chair of the IEEE Future Networks Initiative, and General Chair, IEEE IMS2020

  • Below 6 GHz, 5G deployments will gain momentum in 2020 with many installations across the globe. Meanwhile, mmWave 5G deployments will lag due to challenges of small cell deployment issues, costs, and regulatory hurdles. American carriers like Sprint with sub-6 GHz bands (i.e., 2.5 GHz) may gain an edge since they do not have to deploy small cells so soon.
  • Shared Spectrum Access will be a key technology in the US that will enable sharing of commercial licensed, unlicensed, and government bands. Once proven, this will allow sub-6 GHz spectrum for more rapid deployment, especially in rural regions.
  • Second generation mmWave transceivers will be released in 2020, paving the way to improved performance and much reduced costs for user equipment (UE).
  • What will be the first 5G killer app to gain attention in 2020? AR/VR? Autonomous cars? Enhanced Broadband? MM2M for IoT? Some other use case?

What Innovation in Spectrum Allocation can MeanJIrvine
James Irvine, Co-chair, IEEE Future Networks Initiative - Community Development Working Group, and Reader, Electronic and Electrical Engineering, University of Strathclyde

2020 will be the year when private 5G networks start being taken seriously. While 5G incorporates a range of innovations such as low latency and higher reliability, deployments so far have been very traditional, with mobile network operators (MNOs) adding the technology to their existing networks and focusing on delivering higher speeds. Instrumental in this is that, in general, it is the traditional operators who have access to spectrum. However, across the world, regulators are recognizing the need for innovation in spectrum allocation. For example, the UK regulator Ofcom recently introduced rules for spectrum sharing and the reallocation of existing mobile operator spectrum in areas where it isn’t currently used, with the aim of making local service provision easier. Combining these rules with the more flexible network structure of 5G will make community network providers and 5G private networks a practical possibility. This, in turn, will stimulate the deployment of new, specialized applications such as protection for electrical distribution networks, which 5G makes possible but which aren’t in the plans of traditional operators. As a result, 5G will disrupt the cellular market in a way previous generations have not.


The wild-card for 5G emergence is deployment: 2020 with some creep into 2021 and 2022DavidWitkowski
David Witkowski, Chair, IEEE Future Networks Initiative - International Network Generations Roadmap – Deployment Working Group, and Founder & CEO, Oku Solutions LLC

  • Until the mobile device ecosystem begins widely offering 5G support, initial deployments of 5G will focus on Fixed Broadband as a competitor to xDSL and DOCSIS cable.
  • We expect deployments of 5G Enhanced Mobile Broadband for portable devices will ramp up in late 2020, and initially they will focus on in-building networks (e.g. malls, convention centers, sports venues) and downtown areas with high user densities.
  • Industrial IoT (IIoT) deployments using 5G Ultra-Low Latency Communications (URLLC) and Massive Machine-Type Communications will likewise depend on availability of sensors, actuators, and, in some applications, edge computing. We expect this to begin in 2021 as IIoT device vendors release 5G-enabled products.
  • Availability of 5G URLLC will enable augmented reality (AR) and virtual reality (VR) products – initially for specialized (corporate, medical, government, and public safety) applications and later for consumers as economies of scale bring down costs. We expect some early announcements of 5G-enabled specialized AR/VR in 2020.
  • Citizens Broadband Radio Service (CBRS) will enable private 4G/5G networks and will be disruptive. Device support for the CBRS band will emerge in late 2020, ramping to wide availability in 2021. We expect that in 2022, low-cost consumer-grade CBRS access points will enable homeowners and small businesses (SOHO) to deploy CBRS sites in the same way they currently deploy Wi-Fi access points.
  • Widespread availability of CBRS support in devices will be disruptive to Wi-Fi, especially in enterprise and municipal/public deployments, then in SOHO deployments. Alternative providers now using Wi-Fi First models (Comcast Xfinity Mobile, Google Fi) will shift towards a “Wi-Fi or CBRS First” model, especially if broadband companies add support for CBRS into residential and small business gateways.
  • The wild-card for 5G emergence is deployment. Local governments have struggled with 4G small cell deployments, and the higher density of 5G sites in millimeter wave bands presents additional challenges to application and permitting at the local level. Fears about 5G health effects will require deliberate response from industry, governments, and medical academia to counter misinformation, pseudoscience, and superstition.

Calling for a Sea Change in Transmitter RF EfficiencymcCune
Earl McCune, Ph.D., Co-chair, IEEE Future Networks Initiative - International Network Generations Roadmap – Hardware Working Group, Fellow IEEE, and Chief Technology Officer, Eridan Communications

As more operators push 5G from demonstration sites into wider deployment, 2020 is going to be the year that power efficiency moves to the center of the conversation. Today’s 5G radios are typically operating at about 10% power efficiency, and 5G base stations overall consume about three times as much power as the LTE base stations they replace. Beyond the increased scrutiny that CFOs at mobile operators will be applying to manage the costs of this input power, the waste heat generated by 5G radios is presently imposing substantial design constraints.

But for 5G to reach universal adoption, matching the 20%-range of power efficiency of LTE systems is nowhere near enough. To operate profitably, the industry requires a sea change in transmitter RF efficiency – getting to the neighborhood of 40-60% DC to RF, including all linearization. From small cells that are genuinely small, to cost-effective solar-powered systems, to beam-steering MIMO arrays to cover large open spaces, power efficiency must be at these levels to open up the new deployment options the industry needs.


The 5G Energy Gap – The Bad News and the Good NewsBZ Headshot USE SMALL 7 12 16 1
Brian Zahnstecher, Chair, IEEE Future Networks Initiative - International Network Generations Roadmap – Energy Efficiency Working Group, and Principal of PowerRox

Ok, the bad news first…as these massive 5G networks are being deployed in full speed in 2020, there is the growing issue of the 5G Energy Gap, which is how microwatt-level devices at scale can have a direct impact on the ability of the utility grid to meet the load energy requirements, while maintaining grid reliability. The good news is this is also a fantastic opportunity to fast-track an emerging technology to the mainstream. Energy Harvesting (EH) solutions can supplement or even mitigate the tiny power requirements of systems where it matters most, at the edge. This is done by scavenging every form of physical, ambient energy from the surrounding environment to spare the utility grid and power plants the burden of the 105-106 Power Cost Factor multiples applied to each and every microwatt of edge device received power. Not only will EH sources be a critical factor in addressing the 5G Energy Gap, but this symbiotic relationship will also be mutually beneficial in the respect that increasing viability of the EH ecosystem will also make application to IoT and IIoT devices more pragmatic and affordable. Not only does this lead to a massive environmental impact (i.e., reduction of batteries/hazardous waste/carbon footprint) and increased reliance on sustainable power sources, but also drives critical system design philosophies in power management and energy efficiency.


If and when 5G+ Becomes RealityWaterhouse
Rod Waterhouse, Co-chair IEEE Future Networks Publications Working Group, and, CTO, Octane Wireless

2020 promises to be a very exciting and important year for 5G and future networks. We will definitely see more and more roll out and therefore penetration of the lower spectrum (less than 6 GHz) 5G network throughout the world. Associated with this we will see more debate on the health-related aspects of small cell architectures, whether the debate is founded in science or not. On the research and development side of things, we will see further, exciting activity in the realization of millimeter-wave technology for handsets and access points, and by the year’s end we should be in a better position to see if and when 5G+ (the true incorporation of mm-waves into the mobile network) becomes a reality. Areas of interest to watch over the next 12 months include the role of satellites in future networks, the ramping up of vehicle to X (V2X), the realization of virtual medical care and also efficient technology and protocols for the interface between the backbone and mobile networks. All could be crucial to the success of future networks.


Increasing Demand, Paths of Progress, and New ChallengesWaterhouse
Ashutosh Dutta, Co-chair of the IEEE Future Networks Initiative, and Senior Professional Staff, JHU Applied Physics Laboratory

  • There will be an increased demand for Wi-Fi 6 and private 5G type networks, resulting in co-existence of Wi-Fi and cellular technologies.
  • Security will be embedded in the end-to-end network resulting in more secured 5G networks.
  • There will be an increased trend in virtualizing the network end-to-end.
  • There will be an increased trend in implementation of technologies like Cloud RAN and Mobile Edge Cloud.
  • There will be an increased demand for use of 5G technologies for tactical and first responder networks.
  • There will be a big focus toward sustainability and an increase in activities to spread wireless connectivity in rural networks.
  • There will be increased activities toward implementing experimental testbeds for 5G technologies.
  • Supply chain, geo-political, and environmental issues will be barriers for rapid deployment of 5G technologies.
  • Rural communities will see widespread deployment of low-band networks.
  • Satellite technologies will play an important role in support of 5G use cases.

Learn more about advances to come for 5G and future networks through the International Network Generations Roadmap (INGR), available now on the IEEE Future Networks website.

Featured Video

First Responder and Tactical Networks 2021 - Morning Plenary
14 December 2021, Virtual 

The opening plenary session to the 4th IEEE 5G Workshop on First Responder and Tactical Networks, a partnership and collaboration with the Department of Homeland Security Science and Technology Directorate (DHS S&T), the Johns Hopkins University Applied Physics Lab (JHU/APL), and the Office of Under Secretary of Defense for research and Engineering (OUSD R&E) - 5G.

       
2021 First Responder and Tactical Networks Workshop

Banner 5GWFRTN v3 01

Morning Plenary
Ashutosh Dutta (JHU/APL), Ray Yuan (JHU/APL), Russell Becker (DHS S&T), Mari Silbey (US IGNITE/NSF), Nicholas Oros (FCC), Ari Pouttu (6G Flagship/University of Oulu), Charles Clancy (MITRE)
14 December 2021

Banner 5GWFRTN v3 01

5G Information Overload and the Information Sharing Framework
Ruth Vogel, John Contestabile, Rob Dew, Jay Chang (JHU/APL)
14 December 2021

Banner 5GWFRTN v3 01

Addressing IoT Device Security Concerns in Connected World: Evolutions of Standards, Certifications, Regulations
Anahit Tarkhanyan (Intel)
14 December 2021

Banner 5GWFRTN v3 01

Communication Technologies to Fight Forest Fires
Periklis Chatzimisios, Christos Iliopoulos
14 December 2021

Banner 5GWFRTN v3 01

Adaptable Communication System to the Emergency Scenario
Alessandro Vizzarri; Romeo Giuliano; Franco Mazzenga; Francesco Vatalaro; Anna Maria Vegni
14 December 2021

Banner 5GWFRTN v3 01

HELPS for Emergency Location Service
Hichan Moon (Samsung Electronics)
14 December 2021

Banner 5GWFRTN v3 01

5G Capabilities for First Responders
Brian Daly (AT&T)
14 December 2021

Banner 5GWFRTN v3 01

IEEE MOVE supporting power and communications at disasters
Grayson Randall
14 December 2021

Banner 5GWFRTN v3 01

A Brief History and Future of Police Radio Communications in Hong Kong
Jolly C Wong (Shanghai University)
14 December 2021

Banner 5GWFRTN v3 01

Using PAWR platforms to Explore AI-enabled O-RAN/ONAP-based Disaster Management in 5G Multi-Operator/Multi-Vendor Environments
David Allabaugh (Fujitsu), Martin Skorupski, Alexandru Stancu (Highstreet Technologies), Ivan Seskar (Rutgers University/WINLAB), David Johnson, Jacobus Van der Merwe (University of Utah), Tracy Van Brakle, Giovanni Vannucci (AT&T)
14 December 2021

Banner 5GWFRTN v3 01

IEEE Public Safety Technology Initiative: Emerging Public Safety Technologies and Beyond
Mehmet Ulema, Doug Zuckerman
14 December 2021

Banner 5GWFRTN v3 01

National Security and Emergency Preparedness (NS/EP) Communications – Current and Future Initiatives
Subir Das (Peraton Labs), Frank Suraci (CISA)
14 December 2021

Banner 5GWFRTN v3 01

Non-RT RIC Use Case Service Assurance for First Responder Community
Eugene Gomes and Deepak Kataria (Ericsson)
14 December 2021

Banner 5GWFRTN v3 01

Network Slicing and Traffic Prioritization for First Responder Emergency Services
Eapen Kuruvilla; Denise M.B. Masi; Steven Gordon; Muhammad Hussain; David Garbin
14 December 2021

Banner 5GWFRTN v3 01

Overview of Non Terrestrial Networks
Amitabha Ghosh (Nokia Labs)
14 December 2021

Banner 5GWFRTN v3 01

POWDER Platform: Building Blocks of a Living Lab Enabling Your Research
Kobus Van Der Merwe (University of Utah)
14 December 2021

Banner 5GWFRTN v3 01

Innovating in the Critical Communications Space
Kobus Van Der Merwe (University of Utah)
14 December 2021

Banner 5GWFRTN v3 01

Transforming First Responder Networks to 5G
Kelly Krick (Ericsson)
14 December 2021

Banner 5GWFRTN v3 01

Enabling 5G Expansion into Rural Areas: The Case Study of LibreRouter
Sarbani Banerjee Belur; Dipen Parmar; Tejas Vaghela; Rajesh Kushalkar; Michael Jensen
14 December 2021

Banner 5GWFRTN v3 01

Systems and Networks for Supporting Land SAR Actions in Poland. Perspective of Introducing Testbed for MANET/4G/5G Net to First Responder Duties
Maciej Gucma; Remigiusz Lysik; Miroslaw Radwan
14 December 2021

Banner 5GWFRTN v3 01

Foundational Capabilities for Tactical 5G and Beyond
George F Elmasry; Paul Corwin; Rockwell Collins
14 December 2021

Banner 5GWFRTN v3 01

Performance Evaluation of Quality of Service in 5th Generation Mobile Network
Huiam Eldai; Ibrahim Kh Eltahir
14 December 2021

Banner 5GWFRTN v3 01

Mobile Networks for PPDR/Tactical Use at Work: the Athonet PriMo Solutions
Massimiliano Gianesin; Marco Centenaro; Nicola di Pietro; Daniele Munaretto; Simon O'Donnell
14 December 2021

Banner 5GWFRTN v3 01

Colosseum: How can the World's Largest Network Emulator Accelerate Tactical Network Experimentation
Abhimanyu Gosain (North Eastern University)
14 December 2021

Banner 5GWFRTN v3 01

Dependable 5G Networks for Emergency Applications
Eman Hammad (Texas A&M University System - RELLIS)
14 December 2021

Banner 5GWFRTN v3 01

Enabling Advanced Capabilities via Tactical 4G/5G Cellular Networks
Steve Vogelsang (Nokia)
14 December 2021

Banner 5GWFRTN v3 01

5G & Beyond Security for Mission Critical Communications
Arupjyoti Bhuyan (Idaho National Lab)
14 December 2021

Banner 5GWFRTN v3 01

Future Tactical First Responder Networks: From Spectrum Agility to Network Agility
Apurva Mody (Airnaculus)
14 December 2021

Banner 5GWFRTN v3 01

COSMOS: An Open, Programmable, City-Scale Wireless and Optical Testbed
Ivan Seskar (Rutgers University/WINLAB)
14 December 2021

Banner 5GWFRTN v3 01

Non Terrestrial Networks: Introduction, Applications, and Technology Challenges
Adnan Khan (Anritsu)
14 December 2021

Banner 5GWFRTN v3 01

AERPAW: Aerial Experimentation and Research Platform for Advanced Wireless
Ismail Guvenc (North Carolina State University)
14 December 2021

Banner 5GWFRTN v3 01

Open Source 5G Security Testbed for Edge Computing
Ryan Pepito, Ashutosh Dutta (JHU/APL)
14 December 2021

Banner 5GWFRTN v3 01

Testing and Analyzing 5G Networks
Samir Chatterjee (Rebaca)
14 December 2021

Banner 5GWFRTN v3 01

Edge Services, IAB, and ORAN for the Tactical 5G Networks
Richard Russell (Radisys)
14 December 2021

Banner 5GWFRTN v3 01

Leveraging Physical Layer Security in First Responder and Tactical Networks
Arsenia Chorti (ENSEA)
14 December 2021

Banner 5GWFRTN v3 01

An Open Source 5G-Enabled Edge Cloud
Larry Peterson (ONF)
14 December 2021

Banner 5GWFRTN v3 01

A Comprehensive Evaluation on Multicast and Unicast in Public Safety Communications
Chunmei Liu
14 December 2021

Banner 5GWFRTN v3 01

5G and Beyond Communications Security with Adversarial Machine Learning
Yalin E Sagduyu; Tugba Erpek
14 December 2021

Banner 5GWFRTN v3 01

5G NR and LTE Coexistence in Public Safety Communications
Sneihil Gopal; David Griffith
14 December 2021

Banner 5GWFRTN v3 01

Distributed Beamforming with Autonomous UGVs
Brian M Sadler
14 December 2021

 Banner 5GWFRTN v3 01

Security Analysis of 5G First Responder Networks
Steven Yuen
14 December 2021

 Banner 5GWFRTN v3 01

Network Simulator for Public Safety Communications
Richard Rouil
14 December 2021

Banner 5GWFRTN v3 01

A Study on Using 5GC User Plane Function for Detecting and Monitoring Arrhythmia Symptoms with Portable Single-Lead ECG Devices in Emergency Medical Services
Bhuvaneswari Arunachalan
14 December 2021

 Banner 5GWFRTN v3 01

Design and Development of Compact Microstrip Patch Antennas Using Ceramic Substrates
S. Kannadhasan
14 December 2021

Banner 5GWFRTN v3 01

5G, an Innovative Network for Ghana and Other Parts of Africa
Timothy Kwadwo Asiedu
14 December 2021

Banner 5GWFRTN v3 01

Security Risk Analysis of IoT and Edge Networks
Ashish Kundu (Cisco)
14 December 2021

Banner 5GWFRTN v3 01

Ubiquitous Coverage of 5G through Non-Terrestrial Networks: What is it and How to Prototype it
Raymond Shen (Keysight)
14 December 2021

Banner 5GWFRTN v3 01

Testing and Performance of free5GC
Jyh-Cheng Chen (National Yang Ming Chiao Tung University)
14 December 2021

Banner 5GWFRTN v3 01

Leverage and Enhance 5G/NextG for Tactical Use Through Collaboration
Lizy Paul (National Spectrum Consortium/Lockheed Martin)
14 December 2021

Banner 5GWFRTN v3 01

Deployable Technology Expectations and Realities for the First Responder
Gordon Beattie Jr. (Viavi Solutions)
14 December 2021

Banner 5GWFRTN v3 01

RAN Disaggregation for a Flexible and Reliable Network
Rajat Prakash (Qualcomm)
14 December 2021

Banner 5GWFRTN v3 01

Rescue Services and the Multi-Cloud Service Grid
Sven van der Meer (VMWare)
14 December 2021

Banner 5GWFRTN v3 01

5G Digital Twin and Network Transformation
Glenn Stern (Spirent)
14 December 2021

Banner 5GWFRTN v3 01

Closing Plenary
Sumit Roy (OUSD R&E), Jeff Bratcher (FirstNet), Nada Golmie (NIST), Sean Brassard (JHU/APL), Navin Jaffer (ECD), Scott Fox (DOD), Clarence Huff(DOD), Jorge Pereira (European Commission), Rob Bartholet (JHU/APL)
14 December 2021

   
INGR Webinar Series

FN roadmap apr21 webinar social LI FB1

Cybersecurity and Privacy in Future Networks: Challenges and Opportunities
Ashutosh Dutta, JHU/APL
Eman Hammad, Texas A&M RELLIS
21 April 2021

FN roadmap may19 webinar social LI FB1

Edge Service and Automation for Future Network Generations
Prakash Ramchandran, EOT Found
Sujata Tibrewala, Intel
19 May 2021

FN roadmap jun16 webinar social LI FB1

Open Radio Access Network and Learning Algorithms for Next-Generation Massive MIMO Applications
Chris Ng, Blue Danube Systems
Haijin Sun, U. of Wisc-Whitewater
16 June 2021

FN roadmap July21 webinar social LI FB

Roadmap Towards Federated Testbeds for Future Networks
Ivan Seskar, Rutgers WINLAB
Mohammad Patwary, U of Wolverhampton
21 July 2021

FN roadmap Aug18 webinar social LI FB

The Energy Challenge in Deploying 5G and Beyond
Francesco Carobolante, IoTissimo
18 August 2021

FN roadmap Oct20 webinar social LI FB

Connecting the Unconnected: To Bridge the Digital Divide
Ashutosh Dutta, JHU/APL
Sudhir Dixit, Basic Internet Foundation
20 October 2021

FN roadmap Nov17 webinar social LI FB

AI/ML in the era of 5G, Edge Computing, Open RAN and Hyperscalers
Deepak Kataria, Ericsson
17 November 2021

FN roadmap Dec15 webinar social LI FB

Advanced Solutions for 5G and Beyond Satellite Systems
Prakash Ramchandran, EOT Found
Giovanni Giambene, University of Siena
15 December 2021

FNI webinar Jan19 social

Transdisciplinary Framework for 5G-Enabled Applications and Services in the New Reality
Narendra Mangra, GlobeNet, LLC
19 January 2022

 FNI webinar Feb16 social

How 6G is Reshaping the 5G World Forum
Ashutosh Dutta, JHU/APL
Latif Ladid, University of Luxembourg
Benoit Pelletier, VMware
Aloizio Silva, CCI
16 February 2022

   
2020 First Responder and Tactical Networks Workshop

2020 FRTN Social

Morning Plenary
Ashutosh Dutta (IEEE FNI, JHU/APL); Andrew C. Oak (JHU/APL); Russell Becker (DHS S&T); Dr. Monisha Ghosh (CTO FCC); Dr. Alex Sprintson (NSF); Chris Baker (First Responder)
23 October 2020

2020 FRTN Social

Research Track
Kumar Vijay Mishra (United States Army Research Laboratory, USA); Yalin E Sagduyu (Intelligent Automation, Inc., USA); Tugba Erpek (Virginia Tech, USA); Michele Zorzi (University of Padova, Italy); Michele Polese (Northeastern University, USA), and more
23 October 2020

2020 FRTN Social

Technology Track
Rajeev Gopal (Hughes Network Systems, LLC, USA); Brian Sadler (Army Research Laboratory, USA); Craig A Hendricks (Anritsu, USA) and more
23 October 2020

2020 FRTN Social

Tactical Networks Track I
George F Elmasry (Rockwell Collins, USA); Germano Capela and Luis, Bastos (NATO Communications and Information Agency); Dr. Paul Moakes (CommAgility); Leonid Burakovsky (Palo Alto Networks) and more
23 October 2020

2020 FRTN Social

Tactical Networks Track II
Raymond Shen (Keysight); Jamie Italiano (Verizon); Kiran Mukkavilli (Qualcomm); Doug Kirkpatrick (Eridan Communications) andmore
23 October 2020

2020 FRTN Social

Afternoon Plenary
Dr. Joe Evans (DOD); Ruth Vogel, Cuong Luu, Rob Dew, Jeff Bratcher; Rob Bartholet (JHU/APL)
23 October 2020

   
2020 IEEE 5G World Forum

2020 5GWF Video Benoit Pelletier

Openness and Collaboration Enabling Innovation in the 5G Digital Ecosystem: 2020 5G World Forum keynote series, Benoit Pelletier, Ciena
IEEE 2020 3rd 5G World Forum, 10 - 12 September 2020

2020 5GWF Gerhard Fettweis

6G - Just a better 5G?: 2020 5G World Forum keynote series, Gerhard Fettweis, Technische Universität Dresdan
IEEE 2020 3rd 5G World Forum, 10 - 12 September 2020

2020 5GWF MoU Video Thumbnail

5G World Forum 2020: Multilateral MoU Signing-In Ceremony
IEEE 2020 3rd 5G World Forum, 10 - 12 September 2020

2020 5GWF Closing Video Thumbnail

5G World Forum 2020: 3rd Worldwide 5G Industry Fora Session Closing Remarks
IEEE 2020 3rd 5G World Forum, 10 - 12 September 2020

2020 5GWF Rong Chung Liu and Chung Huan Li

Compact Antenna Test Range Designs for 5G FR1/FR2 OTA Tests: 2020 5G World Forum keynote series, Chung-Huan Li and Rong-Chung Liu, WavePro
IEEE 2020 3rd 5G World Forum, 10 - 12 September 2020

 2020 5GWF Thyaga Nandagopal

AI in 5G Networks: Why, When, and How?, Thyaga Nandagopal, US National Science Foundation
IEEE 2020 3rd 5G World Forum, 10 - 12 September 2020

 2020 5GWF Monisha Ghosh

Blurring the Lines Between Licensed and Unlicensed: 6G or 6GHz, Monisha Ghosh, US Federal Communications Commission 
IEEE 2020 3rd 5G World Forum, 10 - 12 September 2020

 2020 5GWF Chih Lin I

The Next Phase of 5G: Progress, Challenges, and Opportunities, Chih-Lin I, China Mobile
IEEE 2020 3rd 5G World Forum, 10 - 12 September 2020

2020 5GWF Vipin Pande

5G Device Testing, Vipin Pande, Anritsu
IEEE 2020 3rd 5G World Forum, 10 - 12 September 2020

 2020 5GWF Wanshi Chen

Propelling 5G Forward – a close look at R16 and a first look at R17, Wanshi Chen, Qualcomm
IEEE 2020 3rd 5G World Forum, 10 - 12 September 2020

 2020 5GWF Sudhir Kayamkulangara

Birds of a Feather Flock Together: New Age Transport and Multi-Access Edge Compute, Sudhir Kayamkulangara, Cisco Engineering
IEEE 2020 3rd 5G World Forum, 10 - 12 September 2020

 2020 5GWF Sumedha Limaye

5G Infrastructure and Technology for a New World, Sumedha Limaye, Intel India
IEEE 2020 3rd 5G World Forum, 10 - 12 September 2020

2020 5GWF Adrian Scrase

The 5 C's of 5G IoT, Adrian Scrase, ETSI
IEEE 2020 3rd 5G World Forum, 10 - 12 September 2020

2020 5GWF Bilel Jamoussi

Machine Learning for 5G, Bilel Jamoussi, ITU
IEEE 2020 3rd 5G World Forum, 10 - 12 September 2020

 
 2020 5GWF Karim Chaari

Mission Critical Push to Talk (MCPTT) & Secure Communications Services Over Broadband (LTE/5G) for MNO, Governments, and Industries, Karim Chaari, ITU
IEEE 2020 3rd 5G World Forum, 10 - 12 September 2020

 2020 5GWF Jorge Pereira

5G for Connected and Automated Mobility – Challenges towards Deployment: The Cross-border Corridors Use Case, Jorge Pereira, European Commission
IEEE 2020 3rd 5G World Forum, 10 - 12 September 2020

2020 5GWF Radha Krishna Ganti

Low Mobility Large Cell Requirements in 5G, Radha Krishna Ganti, TSDSI
IEEE 2020 3rd 5G World Forum, 10 - 12 September 2020

 2020 5GWF Rob Fish

IEEE Standards Association – A New Era: Raising the World’s Standards, Rob Fish, IEEE Standard Association
IEEE 2020 3rd 5G World Forum, 10 - 12 September 2020

   

2020 Future Networks Videos

IEEE Future Networks Initiative -
Academia and Industry Shaping
and Evolving the Future 

Ashutosh Dutta, Co-chair of
IEEE Future Networks 


The 3 Rs of 5G: Risk Reward & Responsibility 

IEEE Tech Ethics Virtual Panel
Eman Hammad, PwC Canada 
David Witkowski, Joint Venture Silicon Valley  
March 2020
Security in SDNNFV 5G Presentation Video

Security in SDN/NFV and 5G Networks - Opporunities and Challenges, Ashutosh Dutta, JHU/APL
Future Wireless Communication and IoT-5G and Beyond, 7 November 2020, India

UAV Networks Presentation Video

UAV Networks: Architectures, Opportunities, Challenges, and Future, Sudip Misra, Indian Institute of Technology Kharagpur
Future Wireless Communication and IoT-5G and Beyond, 7 November 2020, India

2019 IEEE 5G World Forum

 5GWF19 Video Conference Overview

IEEE 5G World Forum: Enabling 5G and Beyond
Conference Overview, September 2019

 

Ergodic Spectrum Management, John Cioffi, ASSIA 
IEEE 2019 2nd 5G World Forum, 30 Septmber - 2 October 2019, Dresden, Germany 

 

5G is the Greatest Technology Ever (Marketing Meets Engineering), Henning Schulzrinne, Columbia University
IEEE 2019 2nd 5G World Forum, 30 September - 2 October 2019, Dresden, Germany  

 

Randomness: Make It or Use It,  Muriel Médard, Massachusetts Institute of Technology
IEEE 2019 2nd 5G World Forum, 30 September - 2 October 2019, Dresden, Germany 

Is it Time for 6G Yet?, Thyaga Nandagopal, National Science Foundation 
IEEE 2019 2nd 5G World Forum, 30 September - 2 October 2019, Dresden, Germany 

 5G the Journey Continues Chih Lin Video

5G++ the Journey Continues, Dr. Chih-Lin I, China Mobile
IEEE 2019 2nd 5G World Forum, 30 Septmber - 2 October 2019, Dresden, Germany 

5GWF19 Video Fouad El Mernissi

Vertical Industries & 5G Implementation, Fouad El Mernissi, Axians
IEEE 2019 2nd 5G World Forum, 30 Septmber - 2 October 2019, Dresden, Germany 

 5GWF19 Video Tim Hentschel

Research Towards Dependable IoT, Tim Hentschel, Barkhausen Institute
IEEE 2019 2nd 5G World Forum, 30 Septmber - 2 October 2019, Dresden, Germany 

5GWF19 Video Horst Fellner

Autonomous Test & Assurance Solutions, Horst Fellner, Spirent
IEEE 2019 2nd 5G World Forum, 30 Septmber - 2 October 2019, Dresden, Germany 

     

2018 IEEE Workshop on 5G Technologies for Tactical and First Responder Networks 

griffith1stResponders

Modeling Device-to-Device Communications for Wireless Public Safety Networks,  David Griffith, National Institute of Standards and Technology (NIST)
2018 IEEE Workshop on 5G Technologies for Tactical and First Responder Networks

knapp1stResponders

FCC Activities to Support 5G, Julius Knapp, Federal Communications Commission (FCC)
2018 IEEE Workshop on 5G Technologies for Tactical and First Responder Networks

moorefield1stResponders

DoD CIO Brief to JHU APL IEEE 5G Summit, Frederick Moorefield, U.S. Department of Defense
2018 IEEE Workshop on 5G Technologies for Tactical and First Responder Networks

nandagopal1stResponders

Enhancing the Community Response to Aid First Responders, Thyaga Nandagopal, National Science Foundation
2018 IEEE Workshop on 5G Technologies for Tactical and First Responder Networks

nikolich1stResponders

IEEE 802 LAN/MAN Standards, Paul Nikolich, IEEE 802 LSMC Chairman
2018 IEEE Workshop on 5G Technologies for Tactical and First Responder Networks

ratasuk1stResponders

Ultra Reliable Low Latency Communication for 5G New Radio, Rapeepat Ratasuk, Nokia Bell Labs
2018 IEEE Workshop on 5G Technologies for Tactical and First Responder Networks

 rondeau1stResponders 

RF Convergence: From the Signals to the Computer, Thomas Rondeau, DARPA
2018 IEEE Workshop on 5G Technologies for Tactical and First Responder Networks

 samber1stResponders

Path to 5G & Impacts to First Responders: An AT&T Perspective, Chris Samber, AT&T
2018 IEEE Workshop on 5G Technologies for Tactical and First Responder Networks 

 schulzrinne1stResponders

Networks Beyond the Reach of Networks: What Roles Can 5G Play?, Henning Schulzrinne, Columbia University

2018 IEEE Workshop on 5G Technologies for Tactical and First Responder Networks

 simone1stResponders

The Special Needs of National Security and First Responder Communications: Implications for 5G Evolution, Antonio DeSimone, The Johns Hopkins University Applied Physics Laboratory
2018 IEEE Workshop on 5G Technologies for Tactical and First Responder Networks

uhm1stResponders

The Next G: What does 5G mean for Critical Communications and Electromagnetic Spectrum Dominance?, Manuel Uhm, Ettus Research, a National Instruments company
2018 IEEE Workshop on 5G Technologies for Tactical and First Responder Networks 

 zorzi1stResponders

mmWave for Future Public Safety Communications, Michele Zorzi, University of Padova, Italy
2018 IEEE Workshop on 5G Technologies for Tactical and First Responder Networks

brownKipnis1stResponders

Commercial 5G Technology as a Building Block for Tactical Wireless Communication,  Leland Brown and Issy Kipnis, Intel Corporation
2018 IEEE Workshop on 5G Technologies for Tactical and First Responder Networks

  dew1stResponders

Envisioning a Smart Public Safety Ecosystem, Robert Dew, The Johns Hopkins University Applied Physics Laboratory
2018 IEEE Workshop on 5G Technologies for Tactical and First Responder Networks

   

2018 IEEE 5G World Forum 

 

IEEE 5G World Forum: Enabling 5G and Beyond
Conference Overview, July 2018 

 

Welcome Remarks, Ashutosh Dutta, IEEE Future Networks Co-Chair
2018 IEEE 1st 5G World Forum, Santa Clara, California, USA, July 2018 

Facebook 10 Year Roadmap, Jin Bains, Facebook 
2018 IEEE 1st 5G World Forum, Santa Clara, California, USA, July 2018 

 

What's Next for Wireless Research, Monisha Ghosh, National Science Foundation
2018 IEEE 1st 5G World Forum, Santa Clara, California, USA, July 2018 

 

What's Beyond 5G, Andrea Goldsmith, Stanford University 
2018 IEEE 1st 5G World Forum, Santa Clara, California, USA, July 2018 

 

Plotting the Course for Nationwide 5G Deployment, Egil Gronstad, T-Mobile 
2018 IEEE 1st 5G World Forum, Santa Clara, California, USA, July 2018 

 

FCC Spectrum Activities: Fueling the Internet of Things, Michael Ha, FCC 
2018 IEEE 1st 5G World Forum, Santa Clara, California, USA, July 2018 

5G WF 2018 Chih-Lin I

From 'Green & Soft' to Open & Smart', Chih-Lin I, China Mobile 
2018 IEEE 1st 5G World Forum, Santa Clara, California, USA, July 2018  

 

Three Pillars of 5G, Sanjay Jha, Roshmere, Inc. 
2018 IEEE 1st 5G World Forum, Santa Clara, California, USA, July 2018 

 

Evolving 5G & Challenges Ahead, James Kimery, National Instruments 
2018 IEEE 1st 5G World Forum, Santa Clara, California, USA, July 2018 

 

5G Innovations & Challenges, David Lu, AT&T
2018 IEEE 1st 5G World Forum, Santa Clara, California, USA, July 2018 

 

Choosing the Right Connectivity Technology for Your IoT Application, Geoff Mulligan, Skylight 
2018 IEEE 1st 5G World Forum, Santa Clara, California, USA, July 2018 

 

5G & Network Slicing: A New Era in Networking, Constantine Polychronopoulos, VMware 
2018 IEEE 1st 5G World Forum, Santa Clara, California, USA, July 2018 

 

5G & IoT: Cousins Not Sibling, Henning Schulzrinne, Columbia University 
2018 IEEE 1st 5G World Forum, Santa Clara, California, USA, July 2018 

 

Technical Program Overview, Antonio Skarmeta, University of Murcia, Spain 
2018 IEEE 1st 5G World Forum, Santa Clara, California, USA, July 2018 

 

5G Future X Network & the Next Industrial Revolution, Peter Vetter, Nokia Bell Labs 
2018 IEEE 1st 5G World Forum, Santa Clara, California, USA, July 2018 

 

Qiang Ye, Weihua Zhuang, Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada
{q6ye, wzhuang}@uwaterloo.ca

Xu Li, Jaya Rao, Huawei Technologies, Ottawa, Canada
{Xu.LiCA, jaya.rao}@huawei.com

IEEE Future Networks Tech Focus: Volume 3, Issue 1, March 2019 

Abstract 

With the development of software-defined networking (SDN) and network function virtualization (NFV), software-defined topology (SDT) design poses technical challenges in embedding virtual network function (VNF) chains to minimize the embedding cost under packet delay constraints. In this article, we present a novel E2E delay modeling framework for embedded VNF chains to facilitate the delay-aware SDT design. A resource allocation policy called dominant-resource generalized processor sharing (DR-GPS) is applied among multiple VNF chains embedded on a common physical network path to achieve dominant resource allocation fairness and high system performance. An approximated M/D/1 queueing network model is then developed to analyze the average E2E packet delay for each traffic flow traversing an embedded VNF chain.

1. Introduction 

The fifth generation (5G) communication networks are evolving to interconnect a massive number of miscellaneous end devices with diversified service types for Internet-of-Things (IoT) [1]. Machine-to-machine (M2M) communication services and high data rate broadband services are two typical IoT service categories with different traffic statistics and customized end-to-end (E2E) delay requirements. To accommodate an increasing traffic volume from massive IoT devices with differentiated quality-of-service (QoS) demands, the number of network servers providing different functionalities, e.g., firewalls, domain name system (DNS), needs to be increased for boosted network capacity. However, the densified network deployment largely augments both capital and operational expenditure. Software-defined networking (SDN) [2] and network function virtualization (NFV) [3] are two complementary technologies to enhance global resource utilization and to reduce the network deployment cost for service customization, respectively. For the core network, the SDN control module determines the routing path for each service flow based on global network state information. A service (traffic) flow refers to aggregated traffic from a group of end devices belonging to the same service type and traversing the same source and destination edge switches. On the other hand, a centralized NFV control module exists to orchestrate virtual network functions (VNFs) at appropriate general purpose network servers (also named NFV nodes) to achieve flexible service customization. The SDN and NFV control modules are combined as an SDN-NFV integrated controller for VNF orchestration and placement, and traffic routing decisions. At the service level, each service flow is required to pass through a specific sequence of VNFs to fulfill an E2E service delivery with certain functionality and customized QoS requirement. For example, a DNS service flow traverses a firewall function and a DNS function sequentially. A video traffic flow passes through a firewall function and an intrusion detection system (IDS) for a secured E2E video conferencing. We call a set of VNFs interconnected by virtual links as a VNF chain. Software-defined topology (SDT) design studies how to embed each VNF chain onto the physical substrate network to minimize the VNF deployment and operational cost [4].

2. Delay-Aware SDT Design 

For the SDT design, a joint routing and VNF placement problem can be formulated as a mixed integer linear programming (MILP) problem, and a low complexity heuristic algorithm is proposed to solve the problem [4]. The SDT output is the optimal VNF placement on NFV nodes and the optimal traffic routing paths among embedded VNFs. There is an essential tradeoff between minimizing the embedding cost and satisfying the E2E packet delay requirements. To reduce the embedding cost and improve the resource utilization, different VNF chains are preferred to be embedded on a common physical network path with multiple VNFs operated on an NFV node, as shown in Fig. 1. However, the E2E packet delay for each embedded VNF chain can be degraded as it shares both computing and bandwidth resources with other VNF chains.

DelayModelingFig1

Figure 1: Multiple VNF chains embedded on a common physical network path.

 

Existing studies model the E2E packet delay of a traffic flow traversing an embedded VNF chain as the summation of packet transmission delays over each embedded virtual link, without considering the packet processing delay at each NFV node [3], [5]. As a matter of fact, when each packet of a traffic flow traverses an embedded VNF on an NFV node, the packet requires an amount of CPU processing time for certain functionality and an amount of packet transmission time on the outgoing link sequentially. Depending on the type of traversed VNF and the type of service that each flow belongs to, different flows have discrepant time consumption for both CPU processing and link transmission. Some small packets with large header size (e.g., DNS request packets) demand more CPU processing time, whereas other packets with large packet size (e.g., video packets) consume more link transmission time. Therefore, how to allocate both computing and bandwidth resources among the flows traversing the VNF(s) embedded on a common NFV node needs investigation, which affects the packet delay of each flow. More importantly, a comprehensive E2E delay model for packets of a service flow passing through each embedded VNF chain should be established, with the joint consideration of packet processing delays on NFV nodes and packet transmission delays on physical links and network switches (see details in Section III), to achieve delay-aware SDT design.

3. E2E Packet Delay Modeling 

When traversing an embedded VNF, each traffic flow, say flow i, requires different amounts of packet processing time and packet transmission time, denoted by [ti,1,ti,2]. We refer to this time vector as time profile. We define the resource type that a traffic flow consumes more in processing or transmitting one packet as dominant resource. Since different service flows have discrepant time profiles when passing through the VNF(s) on an NFV node, a dominant resource generalized processor sharing (DR-GPS) scheme [6] is employed to allocate the CPU processing resources and the transmission bandwidth resources among different flows. Compared with GPS [7], the DR-GPS is a promising strategy in the context of bi-resource allocation to balance the trade off between fair allocation and high resource utilization. If GPS is directly applied for the bi-resource allocation (i.e., bi-resource GPS), where both processing and transmission rates are equally partitioned among different service flows, the system performance can be degraded due to the discrepancy of time profiles of different flows. In DR-GPS, the fractions of dominant resources allocated to multiple backlogged flows at an NFV node are equalized to ensure the allocation fairness on the dominant resource types (i.e. dominant resource fairness). The fraction of non-dominant resources is allocated to each backlogged flow in proportional to its time profile to eliminate the packet queueing delay before link transmission. When a traffic flow at an NFV node has no packets waiting for processing and transmission, its allocated resources are redistributed among other backlogged flows according to DR-GPS, to improve resource utilization via traffic multiplexing. With the DR-GPS, the processes of packets from each flow traversing the first NFV node V1 of an embedded network path can be modeled as a tandem queueing system, as shown in Fig. 2, where a set of flows traverse V1 and the traffic arrival process for flow i is modeled as a Poisson process with the arrival rate ɣi. The processing and transmission rates allocated to flow i are ri,1 and ri,2, where we have ri,1 =ri,2 according to the DR-GPS. Thus, there is no packet queueing before the link transmission, and packet queueing exists only before the CPU processing.

DelayModelingFig2

Figure 2: A tandem queueing model for traffic flows traversing V1.

Given the set of flows multiplexing at an NFV node, the instantaneous packet processing rate of a tagged flow varies among a set of discrete rate values, depending on the non-empty queueing states of the other flows. This rate correlation makes the queueing analysis intractable for delay calculation. For tractability, we calculate the average packet processing rate for each flow by taking into account the processing queue non-empty probabilities of all the other traffic flows (i.e., exploiting the traffic multiplexing gain), which is used as an approximation of decoupled packet processing rate for the flow [1]. Then, a decoupled queueing model for packet processing of each traffic flow at V1 is established, where the decoupled processing rate for flow i is denoted by di,1, as shown in Fig. 3. To further decouple the transmission rate correlation, we analyze the packet departure process from each decoupled processing at V1. Let Xi be the packet inter-departure time of flow i at the decoupled processing of V1. Due to the Poisson characteristics of the packet arrival process, a departing packet sees the same steady-state queue occupancy distribution as an arriving packet [8]. Therefore, if the mth departing packet sees a non-empty queue, we have Xi = Ti, where TiEqnDelayModeling; if the packet sees an empty queue, we have Xi = Ti+Yi, where Yi is the duration from the mth packet departure instant to the arrival instant of the (m+1)th packet of flow i.  

DelayModelingFig3

Figure 3: A queueing model for decoupled packet processing and transmisson [1]. 

 

Due to the memoryless property,  Yfollows the same exponential distribution as the packet inter-arrival time. Therefore, the probability density function (PDF) of Xi can be calculated as

DelayModelingEqn1v2

where DelayModelingEqn1Sub1, and DelayModelingEqn1Sub2are the PDFs of Yi+T and T, respectively. As Ti and Yi are independent variables, DelayModelingEqn1Sub3 can be calculated as the convolution of the PDFs of Yand Ti [1]. Then, the cumulative distribution function (CDF) of Xi, and its mean and variance are further expressed as [1]

DelayModelingEqn2and3

Eq. (2) and Eq. (3) indicate that the packet inter-departure process from the decoupled processing is a general process between a Poisson process and a deterministic process, with the average departure rate ɣi. Therefore, by using the same method as the processing rate decoupling, we calculate the decoupled packet transmission rate for flow i as di,2, where di,2= di,1. This is because the instantaneous processing and transmission rates are equalized according to DR-GPS, i.e., ri,1 =ri,2, and the average departure rate from each decoupled processing is same as the arrival rate. With the completely decoupled queueing model for both packet processing and packet transmission, the average packet delay, Di,1, for traffic flow i traversing the first NFV node can be determined [1], including packet queueing delay before processing, decoupled packet processing delay, and decoupled packet transmission delay, according to the M/D/1 queueing analysis.

Before modeling the delay of packets traversing the second NFV node V2, we first analyze the packet departure process from the decoupled link transmission of flow i  at V1, which is derived as the same general process with the departure process from the decoupled processing (Analytical details are provided in [1]). The process approaches a Poisson process when ɣi is small and a deterministic process when ɣi is large. Packets from each decoupled outgoing link transmission are then forwarded through a number of network switches and physical links before arriving at the subsequent NFV node. According to Proposition 1 in [1], the packet arrival process of a traffic flow at  Vis the same as the departure process from V1, as long as the transmission rate allocated to the flow at each traversed network switch and link is greater than or equal to the decoupled transmission rate at V1. In this way, no queueing delays are incurred on switches and links, and the bandwidth utilization is maximized. The delay over the embedded virtual links between V1 and V2 can be calculated as the summation of packet transmission delays over network switches and physical links between V1 and V[1]. Since the packet arrival process of each flow at V2 is the same general process in between a Poisson process and a deterministic process with the average rate ɣi, we decouple the processing and transmission rates for flow i  at V2, similar to the rate decoupling at V1. The decoupled rates are denoted by di,1 and di,2, as shown in Fig. 4, where di,1 di,2.

 DelayModelingFig4

Figure 4: A decoupled queueing model for traffic flows traversing  V1 and Vin sequence. 

Since the traffic arrival process at V2 correlates with the packet processing and transmission at V1, a G/D/1 queueing model is not accurate for calculating the delay of packets going through each decoupled processing at V2, especially when ɣi is large [8]. For the case of di,1 di,2, the traffic arrival process of each flow at V2 is more likely to approach a Poisson process with the varying rate parameter ɣi under the queue stability condition [1]. For the case of di,1 ≥ di,2, there is no queueing delay for packet processing at V2. We approximate the packet arrival process of each flow at V2 as a Poisson process with rate parameter ɣi , and establish an M/D/1 queueing model to determine the average queueing delay before processing at V2. Proposition 2 in [1] indicates that the average packet queueing delay, based on the approximated M/D/1 queueing model, provides a more accurate upper bound than that using the G/D/1 queueing model under both lightly- and heavily-loaded input traffic. Therefore, the approximated average packet delay Di,2 , for traffic flow i traversing V2  can be determined [1].

In general, the same queueing modeling methodology can be applied independently at each subsequent NFV node (if any) along the embedded network path, upon which an approximated M/D/1 queueing network is established to calculate the E2E packet delay for each embedded VNF chain. With the proposed analytical E2E packet delay modeling, the delay-aware SDT design can be achieved as illustrated in the flowchart in Fig. 5. First, multiple VNF chains for different E2E service requests are pre-embedded on the substrate network. Then, our proposed delay modeling framework is applied to determine the E2E packet delay for traffic flows traversing the embedded VNF chains. If the E2E packet delay constraints for the flows are satisfied, the delay-aware VNF chain embedding process is completed; otherwise, the VNF chain pre-embedding phase is revisited and the whole process is repeated until delay-aware SDT is achieved.

DelayModelingFig5

Figure 5: A diagram illustrating the delay aware SDT design process. 

 

4. Simulation Results 

In this section, simulation results are provided to verify the accuracy of the proposed E2E packet delay modeling for embedded VNF chains. All simulations are conducted using OMNeT++ [9]. We consider two VNF chains embedded over a common physical network path, as shown in Fig. 1, where flow i  traverses f1  and f2 and flow j  traverses f1  and f2. We test time profiles of the service flows traversing different VNFs over OpenStack [10], a resource virtualization platform for VNF chain orchestration. The testing results and other simulation settings are referred in [1]. We verify the effectiveness of the proposed rate decoupling and delay modeling methods at each NFV node. Packet queueing delay for one of the flows (flow j) before processing at V1 is shown in Fig. 6. It can be seen that the queueing delay derived using the rate decoupling method is close to the simulation results with rate coupling. Packet queueing delay for flow j at V2 is evaluated in Fig. 7, where the queueing delay derived based on the approximated M/D/1 queueing model achieves a much tighter upper bound than that using the G/D/1 queueing model.

DelayModelingFig6

Figure 6: Average packet queueing delay for processing at V1.

5. Conclusion 

In this article, an E2E packet delay modeling framework is established for embedded VNF chains over the 5G core network to facilitate delay-aware SDT design. For the VNF chains sharing resources over a common embedded physical network path, the DR-GPS scheme is employed to allocate the computing resources on network servers and bandwidth resources on outgoing transmission links to achieve dominant resource allocation fairness and high resource utilization. With DR-GPS, an approximated M/D/1 queueing network model is established to analyze the E2E packet delay for traffic flows passing through each embedded VNF chain, which is proved to be more accurate than the G/D/1 queueing model for flows traversing each subsequent NFV node following the first NFV node. Simulation results demonstrate the accuracy and effectiveness of the proposed E2E delay modeling framework, upon which delay-aware SDT can be achieved.

DelayModelingFig7

Figure 6: Average packet queueing delay for processing at V2.

Acknowledgement 

This work was supported by research grants from Huawei Technologies Canada and from the Natural Sciences and Engineering Research Council (NSERC) of Canada.

References 

[1] Q. Ye, W. Zhuang, X. Li, and J. Rao, “End-to-end delay modeling for embedded VNF chains in 5G core networks,” IEEE Internet Things J., to appear, doi: 10.1109/JIOT.2018.2853708.

[2] W. Xia, Y. Wen, C. H. Foh, D. Niyato, and H. Xie, “A survey on software-defined networking,” IEEE Commun. Surv. Tutor., vol. 17, no. 1, pp. 27–51, First Quarter 2015.

[3] F. Bari, S. R. Chowdhury, R. Ahmed, R. Boutaba, and O. C. M. B. Duarte, “Orchestrating virtualized network functions,” IEEE Trans. Netw. Serv. Manage., vol. 13, no. 4, pp. 725–739, Dec. 2016.

[4] O. Alhussein, P. T. Do, J. Li, Q. Ye, W. Shi, W. Zhaung, and X. Shen, “Joint VNF placement and multicast traffic routing in 5G core networks,” in Proc. IEEE GLOBECOM’18, to appear.

[5] L. Wang, Z. Lu, X. Wen, R. Knopp, and R. Gupta, “Joint optimization of service function chaining and resource allocation in network function virtualization,” IEEE Access, vol. 4, pp. 8084–8094, Nov. 2016.

[6] W. Wang, B. Liang, and B. Li, “Multi-resource generalized processor sharing for packet processing,” in Proc. ACM IWQoS’ 13, Jun. 2013, pp. 1–10.

[7] A. K. Parekh and R. G. Gallager, “A generalized processor sharing approach to flow control in integrated services networks: The single-node case,” IEEE/ACM Trans. Netw., vol. 1, no. 3, pp. 344–357, Jun. 1993.

[8] D. P. Bertsekas, R. G. Gallager, and P. Humblet, Data networks. Englewood Cliffs, NJ, USA: Prentice-hall, 1987, vol. 2.

[9] “OMNeT++ 5.0,” [Online]. Available: http://www.omnetpp.org/omnetpp.

[10] “Openstack (Release Pike),” [Online]. Available: https://www.openstack.org.

 

YeDelayModeling

Qiang Ye (S’16-M’17) received his Ph.D. degree in electrical and computer engineering from the University of Waterloo, Waterloo, ON, Canada, in 2016. He is currently a Research Associate with the Department of Electrical and Computer Engineering, University of Waterloo, where he had been a Post-Doctoral Fellow from Dec. 2016 to Nov. 2018. His current research interests include AI and machine learning for future wireless networking, IoT, SDN and NFV, network slicing for 5G networks, VNF chain embedding and end-to-end  performance analysis.

 

 

zhuangDelayModeling

Weihua Zhuang (M’93-SM’01-F’08) has been with the Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada, since 1993, where she is a Professor and a Tier I Canada Research Chair in Wireless Communication Networks. She is the recipient of 2017 Technical Recognition Award from IEEE Communications Society Ad Hoc & Sensor Networks Technical Committee, and a co-recipient of several best paper awards from IEEE conferences. Dr. Zhuang was the Editor-in-Chief of IEEE Transactions on Vehicular Technology (2007-2013), Technical Program Chair/Co-Chair of IEEE VTC Fall 2017 and Fall 2016, and the Technical Program Symposia Chair of the IEEE GLOBECOM 2011. She is a Fellow of the IEEE, the Royal Society of Canada, the Canadian Academy of Engineering, and the Engineering Institute of Canada. Dr. Zhuang is an elected member in the Board of Governors and VP Publications of the IEEE Vehicular Technology Society. She was an IEEE Communications Society Distinguished Lecturer (2008-2011). 

 

LiDelayModeling

Xu Li is a staff researcher at Huawei Technologies Inc., Canada. He received a Ph.D. (2008) degree in computer science from Carleton University. His current research interests are focused in 5G system design and standardization, along with 90+ refereed scientific publications, 40+ 3GPP standard proposals and 50+ patents and patent filings. He is/was on the editorial boards of the IEEE Communications Magazine, the IEEE Transactions on Parallel and Distributed Systems, among others. He was a TPC co-chair of IEEE VTC 2017 (fall) – LTE, 5G and Wireless Networks Track, IEEE Globecom 2013 – Ad Hoc and Sensor Networking Symposium.

 

RaoDelayModeling

Jaya Rao (M'14) received his Ph.D. degree from the University of Calgary, Canada, in 2014. He is currently a Senior Research Engineer at Huawei Technologies Canada, Ottawa. Since joining Huawei in 2014, he has worked on research and design of CIoT, URLLC and V2X based solutions in 5G New Radio. He has contributed for Huawei at 3GPP RAN WG2, RAN WG3, and SA2 meetings on topics related to URLLC, network slicing, mobility management, and session management.

 

 

Editors: Chih-Lin I and Haijun Zhang

Chih-Lin I, Junshuai Sun, Xingyu Han, Yingying Wang, Xueyan Huang, Green Communication Research Center, China Mobile Research Institute
{icl, sunjunshuai, hanxingyu, wangyingying, huangxueyan} @chinamobile.com

IEEE Future Networks Tech Focus: Volume 2, Number 3, December 2018 

Abstract
5G provides the capability to support various services, which means the original one-size-fits-all architecture and functions cannot satisfy the diversified requirements of different scenarios. As one of the most promising service-oriented technologies, end-to-end network slicing was put forward to support the 5G provisions. As a vital part of the end-to-end slicing, the RAN slicing is still under-developed. This article provides a detailed investigation on the RAN slicing with functional explorations and operational procedures, hoping to give heuristic approaches to the implementation of the RAN slicing.
Keywords: RAN slicing, OAM, QoS

1. Introduction
The 5G era is coming near. As an evolutional generation of the mobile network compared to 4G, 5G is required to support vertical industrial scenarios, which gives 5G the strong capability to explore the blue sea of the telecommunication industry. Consequently, the whole network is demanded to become more service-driven and user-centric. Under this circumstance, end-to-end network slicing was proposed to satisfy the above-mentioned characteristics of the new network.

From the operator’s perspective, the end-to-end mobile network is made up of three parts: the core network (CN), the transmission network (TN) and the radio access network (RAN). According to the current progress of standardization on slicing, the detailed description on the network slicing in CN has been adopted by 3GPP SA2 in TS23.501 [1]. In addition, ITU-T SG-15 recently approved the proposal on the Slicing Packet Network (SPN) [2] as a candidate scheme of TN for further study. Compared to the rapid standardization pace in CN and TN, the work on the RAN slicing is comparatively slow in progress.

Different from CN and TN, the characteristic of the air interface indicates that the sharing of radio resources provides the most efficient way of resource utilization, which is still the design logic of 5G New Radio (NR), i.e., 5G RAN. Although 3GPP RAN3 has given several solutions on the setup procedure for the RAN slicing instance in the Study Item (namely, TR38.801 [3]), this topic is still controversial, especially on the detailed descriptions of the supported functions, let alone considering the interaction with Operation, Administration and Maintenance (OAM) entity.

2. Why investigate RAN slicing?
One may argue that there’s no need to adopt the RAN slicing, which can be replaced by precise QoS manipulation of Data Radio Bearers (DRBs) without breaking the design logic of resource sharing. In fact, it cannot be denied that the sharing on resources including calculation, storage, radio and frequency maximizes the network utilization. After the introduction of the RAN slicing, the base-stations have to be able to configure more dedicated resources, which may have an impact on the resource exploitation. From the perspective of operators, however, providing customized services in terms of dedicated resources indicates a possibility to improve the Quality-of-Experience (QoE) for users, compared to the strategy of the total resource sharing. In addition, the RAN awareness of the slice information is suitable for the adoption of the user-centric network, enabling the RAN to make better scheduling judgment, which trades off between the resource utilization and the user satisfaction.

It should be noted that several attempts on Proof-of-Concept (PoC) tests on the RAN slicing have been carried out in academia [4]-[6], including the theoretical analysis, the algorithmic investigation, and the prototype demonstration, etc. All of them have shown remarkable performance improvement, which indicates the practicability of the RAN slicing; however, none of them have provided a systematic exploration on the feasible functions for the RAN slicing, nor did they propose any operational procedures. As a result, this article is aiming at offsetting these gaps by providing detailed discussions in the following two parts.

3. Functional exploration for the RAN slicing
In this part, the possible functions related to the RAN slicing are explored on the gNB (i.e. the base-station of 5G NR) side not only in terms of services, but also in terms of the OAM, which could be constructive to operators. The basic framework for the functional exploration is shown in Fig.1.

Figure 1: The proposed functions related to the RAN slicing

 Fig. 1: The proposed functions related to the RAN slicing

 

Firstly, the OAM-related functions are discussed, which can be further divided into Equipment Management (EM), Network Management (NM) and Deployment Management (DM), etc. The detailed descriptions are given below:

  • EM: With the introduction of the RAN slicing, the operators should be able to perform the slice-level EM besides the classical OAM functions such as equipment status monitoring. In addition, when the system is evolved to the cloud platform, the OAM should be able to independently manage multiple Network Functions (NFs), which may be operated on the same general device but belong to different slices. In a word, EM should be able to achieve administration and control of all types of devices if the RAN slicing is adopted.
  • NM: NM is responsible for the partition and distinction of applicable scopes for different slices while maintaining the isolation of NFs. In order to reflect the idea of user-centric network, it is inspiring for the RAN to decouple NFs from classical network entities. Therefore, the vertical industrial slice and the common communication slice can be implemented by two independent sets of NFs, which brings convenience compared to the management of network entities.
  • DM: DM is in charge of the deployment of NFs or network entities according to the requirement of the slice from CN. For example, for URLLC slices, the functions of the protocol stack should be deployed on devices which are close to the air interface as much as possible, in order to guarantee the low latency of the transmission. In a word, the deployment ways are diversified for NFs, and most importantly, DM needs to ensure the accuracy and the stability on controlling the NFs.

Secondly, the service-related functions are investigated, which is composed of User Management (UM), Function Management (FM), Radio Management (RM) and QoS Management (QM). The separate descriptions are obtainable as follows:

  • UM: UM is in charge of the storage and the maintenance of UE context according to the characteristics of users and the slice information. In addition, UM is responsible to perform differentiated configurations for Radio Bearers (RBs) and Cell Groups (CGs). If a specific user is configured with multiple slices which belong to different network tenants, UM should be able to achieve the isolation and the security protection of the user information, while satisfying different targeting requirements for different tenants.
  • FM: FM provides the differentiated settings on functions of the protocol stack for different slices. For example, for URLLC slices, the Duplication function should be configured at the PDCP layer in order to satisfy the ultra-high reliability; while for mMTC slices, the DRX function with differentiated settings should be adopted and separately configured in order to satisfy different levels of energy-saving. The extension is driven by use cases, which provides sufficient space for further exploration.
  • RM: As mentioned above, the adoption of the RAN slicing introduces possible management on dedicated resources, and the adaptation to which requires the concept redefinition and the algorithmic improvement. For example, since the adopted 100MHz bandwidth is sometimes too much for a service, the whole bandwidth can be partitioned into several smaller service-oriented bandwidth parts based on the slice information. In addition, RM should also support the soft isolation of bandwidth parts subject to different tenants.
  • QM: QM is responsible to provide reasonable slice-level QoS profiles. In addition, for new slice type provided by a tenant, QM needs to realize the feasible quantified QoS definition within the scope of RAN, according to the OAM-related weight for this tenant and QoS Flow/DRB (Data Radio Bearer)-level QoS profiles within the slice, and guarantees the QoS characteristics of this slice.

In summary, the above analysis aims at inspiring the work on the functional exploration subject to the RAN slicing, and the feasible functions are not restricted to ones listed above; hopefully more extensions could be introduced for further study.

4. The setup/modification procedure for the RAN slicing instance
This part gives an overall setup/modification procedure for the RAN slicing instance which is shown in Fig. 2; in particular, the procedure involves the interaction with OAM from the operator’s perspective. Generally speaking, the procedure is triggered by NSSAI sent from 5G Core (5GC). And the gNB is responsible to produce the scheme to support the RAN slicing, with the aid of OAM-related and service-related functions specified above and configure corresponding UEs through Radio Resource Control (RRC) signaling.

Firstly, the UE interacts with 5GC to perform the selection of Access and Mobility Management Function (AMF) through Non-Access Stratum signaling (which is transparent to RAN and UE Access Stratum), determining requirements of the end-to-end network slicing based on the selected S-NSSAI, which may imply the isolation information for this slice.

Next, 5GC informs RAN to setup or modify the RAN slicing instance which is within RAN’s capability by sending NSSAI. An NSSAI may contain multiple S-NSSAIs, each of which corresponds to a specific SLA. Note that the NSSAI can be carried on different messages (which are given in Fig. 2) by listing all S-NSSAIs for each PDU session. After RAN obtains the NSSAI, the six-step procedure for the RAN slicing starts.

Figure 2: An overall setup/modification procedure for the AN slicing instance

Figure 2: An overall setup/modification procedure for the RAN slicing instance

  • Step 1: RRC sends NSSAI Request message to RRM.
    After acquiring NSSAI, in order to satisfy the slice-level QoS profile, RRM generates/selects parameters, functions and algorithms which may contain:
    1. The management parameters including UE context and slice-level QoS profile.
    2. The functional parameters including configured functions subject to specific RB/Logical Channel (LCH) within the slice, and the mapping between RBs and LCHs.
    3. The selected algorithms subject to the RAN slicing including bearer management part and resource management part, from which the bearer management part further contains algorithms for Admission Control, Bearer Control and Handover; while the resource management part further contains algorithms for Scheduling, Power Control, Interference Coordination and Load Balancing (which indicates that RRM contains both RRC-level and MAC-level controlling operations).
  • Step 2: RRM sends NSSAI Response message back to RRC.
  • Step 3: RRC sends OAM for RAN Slicing Request message to OAM which contains corresponding slice information.
    After receiving this message, according to the operator’s strategy, OAM generates parameters and scheme for the RAN slicing which may contain:
    1. Setting up independent strategy for tenants: OAM is able to define specific priorities for different slices for each tenant, providing customized guarantees on the air interface. In addition, OAM ensures the isolation on the equipment and the network, while trying best to provide high flexibility for the deployment of NFs. If the slice requirement matches the one in the stored slicing template, it is suggested that the stored template can be reused.
    2. Performing operations related to EM, NM and DM for the RAN slicing, such as providing a combinatorial set of equipments and NFs for specific types of slices.
  • Step 4: OAM sends OAM for RAN Slicing Response message back to RRC. If the message indicates a failure, a cause value should also be included in the response.
    (Note that, in our opinion, the RRM configuration and OAM configuration procedures are independent, which means Step 1-2 and Step 3-4 could be executed concurrently.)
  • Step 5: After the completion of Step 1-4, RAN is able to support the specified RAN slicing. Then RRC sends RAN Slicing Configuration message to configure L2 (SDAP/PDCP/RLC/MAC) and L1 (PHY) on the gNB side.
  • Step 6: gNB sends RAN Slicing Setup/Reconfiguration message to UE, triggering the RAN slicing setup/reconfiguration procedure which requires the interaction between gNB and UE AS. During the procedure, UE achieves the configuration on parameters and functions for the protocol stack which is indicated by RRC signaling on the gNB side.

After the completion of the above 6 steps, RAN sends response back to CN, which means the successful establishment/modification of the RAN slicing instance.

5. Conclusion
The RAN slicing contains huge potential to be one of the most practical technologies in 5G NR. As a result, it is believed that work on the RAN slicing is becoming more and more valuable. This article presents opinions on functional exploration and procedural descriptions on the RAN slicing from the perspective of operators, which only makes a quick glance at current progress on the RAN slicing. With deeper investigations on related topic, there are many more standardization and algorithmic research to study on. In order to clarify the problems related to the RAN slicing, any further technical discussions from any organizations are welcomed.

References

  1. 3GPP TS 23.501: “System Architecture for the 5G System; Stage 2”, V15.0.0 (2017-12)
  2. China Mobile Communications Corporation, “Technical Vision of Slicing Packet Network (SPN) for 5G Transport”, V1.0 (2018-02)
  3. 3GPP TR 38.801: “Study on New Radio Access Technology; Radio Access architecture and interfaces”, V14.0.0 (2017-03)
  4. Kokku, R, et al. "CellSlice: Cellular wireless resource slicing for active RAN sharing." Fifth International Conference on Communication Systems and Networks IEEE, 2013:1-10.
  5. Foukas, Xenofon, et al. "Orion: RAN Slicing for a Flexible and Cost-Effective Multi-Service Mobile Network Architecture." The, International Conference 2017:127-140.
  6. Ksentini, Adlen, and N. Nikaein. "Toward Enforcing Network Slicing on RAN: Flexibility and Resources Abstraction." IEEE Communications Magazine 55.6(2017):102-108.

 

chih lin I croppedChih-Lin I received her Ph.D. degree in electrical engineering from Stanford University. She has been working at multiple world-class companies and research institutes leading the R&D, including AT&T Bell Labs; Director of AT&T HQ, Director of ITRI Taiwan, and VPGD of ASTRI Hong Kong. She received the IEEE Trans. COM Stephen Rice Best Paper Award, is a winner of the CCCP National 1000 Talent Program, and has won the 2015 Industrial Innovation Award of IEEE Communication Society for Leadership and Innovation in Next-Generation Cellular Wireless Networks.

In 2011, she joined China Mobile as its Chief Scientist of wireless technologies, established the Green Communications Research Center, and launched the 5G Key Technologies R&D. She is spearheading major initiatives including 5G, C-RAN, high energy efficiency system architectures, technologies and devices; and green energy. She was an Area Editor of IEEE/ACM Trans. NET, an elected Board Member of IEEE ComSoc, Chair of the ComSoc Meetings and Conferences Board, and Founding Chair of the IEEE WCNC Steering Committee.

She was a Professor at NCTU, an Adjunct Professor at NTU, and an Adjunct Professor at BUPT. She is the Chair of FuTURE 5G SG, an Executive Board Member of GreenTouch, a Network Operator Council Founding Member of ETSI NFV, a Steering Board Member and Vice Chair of WWRF, a Steering Committee member and the Publication Chair of IEEE 5G Initiative, a member of IEEE ComSoc SDB, SPC, and CSCN-SC, and a Scientific Advisory Board Member of Singapore NRF. Her current research interests center around “From Green & Soft, to Open & Smart”.

JunshuaiSun Junshuai received the M.S. degree in CST from Xidian University, Xi’an, China, in 2005. From 2005 to 2013, he worked in CATT as a TD-SCDMA/TD-LTE L2 engineer, SE, team leader and the director of high layer technology department. Since 2013, he has worked as a researcher in CMRI. He has great R&D and industry experience in both telecommunication and radio resource management, based on which he puts forward MCD (Multiple centralized and distributed) design logic of the protocol stack. His current research interests focus on the architecture and functionalities of wireless protocol stack.

 

 

JunshuaiXingyu Han received the Ph.D degree in electronic engineering from the department of EECS, Queen Mary University of London, UK, in 2016. Since 2017, he has been working as a researcher and project manager in Green Communication Research Center of China Mobile Research Institute, focusing on promoting the development of the protocol stack for 5G NR and beyond. He is now tracking the progress of 3GPP RAN2&3 and contributing to the related Working Groups. His current research interests include the system design of the protocol stack, the innovation of RAN architecture and the application of Wireless Big Data.

 

 

JunshuaiWang Yingying received the B.S. degree and M.S. degree in Communication and Information System from Xidian University, Xi’an, China, in 2007 and 2010. From 2010 to 2015, she worked in NPC and Spirent as a senior LTE L2 software engineer. Since 2015, she has worked in CMRI as a Wireless access network researcher. She has great research and industry experience in telecommunication. She focuses on the architecture and functionalities of wireless protocol stack of RAN.

 

 

JunshuaiHuang Xueyan received the master degree in wireless communication from Beijing University of Posts and Telecommunications, Beijing, China, in 2015. From October 2015 to 2018, she was a protocol researcher of China Mobile Research Institute, focusing on user plane protocol stack research. She has more than one year 3GPP experience, and mainly follows RAN3 CU/DU architecture/interface and RAN2 user plane function design and optimization.

 

 

Editor: Anwer Al-Dulaimi 

 

Yan Wang, Hua Huang, Yingzhe Li, Wei Zhou, Wireless Network Research Department, Huawei Technologies, Shanghai, China
{jason.wangyan, hua.huang, yingzhe.li, will.zhou} @huawei.com

Chih-Lin I, Qi Sun, Siming Zhang, China Mobile Research Institute, Beijing, China
{icl, sunqiyjy, zhangsiming} @chinamobile.com

IEEE Future Networks Tech Focus: Volume 2, Number 3, December 2018 

Abstract

The rejuvenation of AI technology provides a new way to solve the increasingly complex and difficult problems in the 5G network, which makes the network more intelligent and autonomic. This field has gradually become a hot topic both in the academia and industry. But the industry has not yet reached a unified definition of an intelligent mobile network, and how to measure and judge the level of intelligence. This paper attempts to give the definition of the levels of mobile network intelligence and analyzes the influence of intelligence on the evolution of wireless network architecture, hoping to help the industry to reach consensus.

1. The state of the art of the mobile network with AI
In the evolution process from 4G to 5G, the performance and flexibility of wireless networks have changed fundamentally. For the performance, to support the three typical services of enhanced Mobile Broad Band (eMBB), massive Machine Type Communications (mMTC) and Ultra-Reliable and Low Latency Communication (URLLC), 5G network introduces advanced technologies such as large-scale antenna array, flexible air interface and Non-Orthogonal Multiuser Access technology to meet the more stringent technical requirements in terms of peak rate, spectrum efficiency, low delay, high reliability, connection density. For the flexibility, various decoupling are happened in the 5G architecture, such as software and hardware decoupling for the Network Function Virtualization (NFV), control and forward decoupling for the gateway, control plane function decomposition, Central Unit (CU) and Distributed Unit (DU) separation of Radio Access Network (RAN), etc. This makes network functions easier to deploy on the cloud computing platform, enabling automated orchestration and deployment of network functions to provide efficient network slicing services for different vertical application requirements.

Although 5G has brought a qualitative leap in performance and flexibility, the richer KPI dimensions, flexible air interface, virtualization of network function and the introduction of slicing technology lead to the utmost complexity and challenges of the design, deployment operation and optimization of the 5G network. Artificial Intelligence (AI) technology, which is rejuvenated by the great development of big data, deep learning and cloud computing, provides a data driven methodology worthy of exploration for solving the complicated problems that 5G network faces [1]. The application of AI to wireless networks has attracted more and more attention in the academic field. There are many research literatures in AI assisted New Radio (NR) resource allocation, cloud resource management, receiver design, channel parameter estimation and so on[2][3][4][5].

However, mobile network empowered by AI is not a single point technology or internal implementation problem but requires systematic thinking on the architectural level. 5G Americas proposed that orchestration, analytics and automation enabled by AI or Machine Learning (ML) will play a key role in 5G network [6]. Some research projects of 5G Infrastructure Public Private Partnership (5GPPP) tries to use AI and ML to a achieve real time autonomous 5G network management [7][8][9]. Many pre-standard and standardization organizations have carried out discussions and research items on network intelligence based on Big data and AI technology. For example, the European Telecommunications Standards Institute (ETSI) set up the Experiential Network Intelligence Industry Specification Group (ENI ISG) and the Zero touch network and Service Management Industry Specification Group (ZSM ISG) focusing on intelligent closed loop policy and fully automatic network management, respectively[10][11]. ITU-T set up Focus Group on Machine Learning for Future Networks including 5G (FG-ML5G) investigating on valuable use cases, data model and algorithms, and network intelligent architecture [12]. 3GPP has approved data driven related study items, e.g., Enables of Network Automation in 3GPP SA2 working group [13] and RAN-Centric Data Collection and Utilization in 3GPP RAN3 working group [14].

2. The Lack of unified definition for mobile network intelligence
Currently different organizations and research institutions have different views on the application of AI into the mobile network. They pay more attention to the specific layer or domain in which the AI or Big data can be used to improve the efficiency and performance, making the research relatively fragmented. For instance, ETSI ENI ISG mainly focuses on policy architecture, ETSI ZSM ISG focuses on management, 3GPP focuses on control plane and Self-Organized Network (SON), and academic community pays more attention to the physical layer and new application scenarios (such as cache in wireless network).

Can the mobile network be considered as intelligent when the AI is only used in mobile networks to solve certain specific problems at certain specific layer or domain? Let's first examine the following questions:

  • Is AI enabled operation and maintenance an intelligent network? The intelligent operation and maintenance is the primary value of the AI for network. AI helps to improve the optimization of parameter configuration and the efficiency of fault prediction and diagnosis, minimize the manual intervention and reduce OPEX. However, intelligent operation and maintenance only maintains the existing network to its best level and does not make the network itself to have intelligence such as context awareness, service awareness, and automatic policy control and resource scheduling.
  • Is AI enabled SON an intelligent network? Self-Organizing Network (SON), defined by 3GPP, includes self-configuration, self-optimization and self-healing. In fact, it has embodied the characteristics of network intelligence. But 3GPP's definition of SON is limited to certain specific features, such as the Automatic Neighbor Relation (ANR), Mobility Load Balancing (MLB), Mobility Robustness/Handover Optimization (MRO), Inter-Cell Interference Coordination (ICIC), Coverage and Capacity Optimization (CCO), Cell Outage Detection (COD), Cell Outage Compensation (COC) and so on [15]. These SON features usually designed independently and heavily rely on the standardization resulting in a bundle of chimney-like features. AI enabled SON may be just a better SON, but it won’t change its limited scenarios and dependence on the standard process.
  • Is AI enabled Radio Resource Management (RRM) or Radio Transmission Technology (RTT) an intelligent network? Recently, academic community have tried to study AI technology in radio resource management level and physical level, such as Adaptive Modulation and Coding (AMC), Massive MIMO beam forming, etc., to exploit potential performance with reduced complexity [16]. For these research, AI is more of an auxiliary means of existing algorithm or an alternative to the traditional algorithm, which cannot be regarded as a system-level intelligence from the whole network perspective.

In summary, the use of AI technology at any layer or domain of the mobile network can solve some complex problems and bring a certain degree of intelligence, which unfortunately does not demonstrate the intelligence of the whole network. Network intelligence should be a system-level concept, not a single function. This leads to several further questions:

  • What is the ultimate goal of realizing wireless network intelligence with AI?
  • How to evaluate the level of wireless network intelligence?
  • What is the impact of different level of intelligence on the existing wireless network architecture?
  • How will the network architecture evolve towards the goal of intelligence?

3. The definition of mobile network intelligence
The grading definition of automated driving provides a good reference for us to understand and judge the levels of intelligence of mobile networks. To unify the understanding of the concept of automated driving in the whole industry, the SAE (International Automotive Engineering Society) J3016 document provides a taxonomy with detailed definitions for 6 levels of driving automation [17]. The proposal has become a widely accepted standard in the vehicle industry and is used to guide the vehicle industry to carry out five phases of work for driving automation in stages [17].

Introducing intelligence into vehicles achieves fully automated driving, while the goal of wireless network intelligence is the "network autonomy". The network can automatically deploy, configure, and optimize by itself to achieve target KPI according to the intention of the operators, and can automatically avoid or solve abnormal events to ensure the security and reliability of the network. But the ultimate objective of full autonomy cannot be achieved overnight. It needs to be implemented step by step. Borrow the idea from SAE, we also need to define several features related to the level of mobile network intelligence and determine the level of network intelligence by analyzing the degree of substitution for AI subsystem with the defined features.

Here we try to give 7 features for investigating network intelligence levels:

  1. Context awareness and analysis. It is to tell what happened inside the network and what is the root cause;
  2. Non-real time prediction and inference. It is to judge what will happen in the network management plane in future (maybe several minutes or hours later);
  3. Decision-making and execution. It is to make policy and take control measures automatically based on the prediction and reasoning;
  4. Real-time prediction and inference. It mainly aims at prediction and inference at milliseconds or microsecond scale at L1-L3 layer of RAN with real-time control and scheduling;
  5. Exception handling. It refers to the handling of sudden or extreme events and recovery from abnormal outage;
  6. Human-Network Interface. It refers to whether operator interact with the network through traditional specialized signaling or command interaction, or through intention-based interface;
  7. Applicable scenarios. It refers to whether the intelligent scenarios cover specific functionalities, specific services, or integrated scenarios in the complete network life cycle.

Based on the degree of substitution for the AI subsystem in the 7 defined features, we can classify the intelligence of the mobile network into 6 levels, as shown in Figure 1.

 Figure 1. The levels of mobile network intelligence

 Figure 1. The levels of mobile network intelligence

As shown in Fig. 1, the higher the level of network intelligence, the more work of human operators are replaced by AI subsystems in the 7 features. It is worth noting that a certain level of intelligence is reached only when all the features of this level are implemented by the AI subsystem. For example, to achieve level 2 intelligence, the AI subsystem must simultaneously support feature 2 and feature 3. However, if the AI subsystem only supports part of the feature required, it cannot be regarded as Level 4 intelligence.

The benefits of such intelligence grading are as follows:

  • It helps the industry to reach a consensus of the definition of intelligent wireless networks;
  • It provides some criteria for judging the development level of the wireless network intelligence;
  • It provides decision-making basis for the government, operators, equipment vendors and other related industry partners to do technology selection, product planning, etc.

4. The impact on the mobile network architecture evolution

Introducing Big data and AI technology into wireless networks to achieve network intelligence will definitely impact the existing mobile network architecture. We believe that AI subsystems will be introduced in the three domains of the network, i.e., the Operations Support System (OSS) plane, the Core Network (CN), and the Radio Access Network (RAN.) 3GPP has approved two study items related to network data analysis, i.e., Enabler of Network Automation in SA2 and RAN-Centric Data Collection and Utilization in RAN3, which will accelerate the standardization pace. The core network side has defined the Network Work Date Analysis (NWDA) function as the hosting entity of big data and AI subsystem. The RAN side study item which approved in June may also study the possibility of introducing a Radio Data Analytics (RDA) function (i.e. NWDA-like functional entities) on the RAN side.

 Figure 2. The Introduction of AI subsystem in the 3GPP network architecture

 Figure 2. The introduction of AI subsystem in the 3GPP network architecture

 

The application scenarios and intelligent level that each domain requires are different, as shown in Figure 2. At the OSS, it usually covers the network planning related use cases, including coverage prediction, fault diagnosis, cell edge throughput enhancement, and virtual grid based multi frequency parameter optimization and so on. The prediction is non-real-time and usually beyond minutes’ level. According to the aforementioned intelligent level definition, the OSS intelligence can reach level 3 at the most because it cannot achieve near-real-time and real-time prediction and inference needed at the control layer and the physical layer. 5G CN with introduced AI subsystem can achieve control plane related intelligent scenarios, such as the intelligent QoS control, the personalized mobility management, the load-balance of the Virtualized Network Function (VNF), network traffic prediction, etc. The prediction timescale is usually larger than second level. Since the core network cannot handle the real-time resource control at the TTI level in RAN, the core network intelligence can reach level 3 or at most level 4. If the AI subsystem is introduced in the RAN, it can handle the near real-time and real-time RRM/RTT related intelligent scenarios, including Smart AMC, intelligent multiuser Pairings, AI decoders, deep learning based digital pre-distortion, etc., and the prediction timescale can be even reduced to the microsecond level. It is possible to realize intelligence of level 4 at most since only the scenarios within RAN are considered.

Only when the AI subsystems of all three domains collaborate, the network intelligence of level 5, i.e., the fully autonomous network, can be reached.

 Figure 3. The architecture evolution map according to the network intellgent levels

Figure 3. The architecture evolution map according to the network intelligenct levels

Based on the above analysis, Figure 3 shows the relationship between the architecture evolution and network intelligent grading. If only the network intelligence or autonomy of L1~L3 level is needed, there is no impact on the 3GPP network architecture, and the intelligence is mainly embodied in the OSS, or the management and orchestration layer. At this stage if the AI technology is utilized to improve the performance and efficiency within base station, since this is only an internal implementation issue, there is still no need for architectural support.

However, if we need to achieve intelligence above level 3, network architecture is required to be upgraded. To achieve Level 4 intelligence, we need to further introduce AI subsystem in CN and RAN to increase near-real-time and real-time prediction and reasoning capabilities. In addition to the interaction between the three AI subsystems, it may be necessary to interact between the wireless network AI system, cloud platform and UE to achieve    cross-layer or cross-domain coordination, thereby significantly expanding the network intelligent application scenarios. For the Level 5 intelligence, the AI will be already a ubiquitous capability of the network, and it will bring about a fundamental change in the human-network interface, realizing an intent-driven network, free of manual operation and maintenance. To realize the network intelligence of Level 4-5, the network architecture will have great changes in functions, interfaces and procedures, as well as innovations in chips and algorithms.

5. Conclusions

The introduction of AI into mobile networks has been highly expected by the industry for solving complex problems at various network levels, and ultimately enabling an intelligent and fully autonomous network. This paper attempts to give a definition and grading of mobile network intelligence from a research perspective. We propose a taxonomy with detailed definitions for 6 levels of intelligence and 7 key features to be fulfilled. Hopefully such discussion will serve as an anchor in reaching a unified understanding of the definition of intelligent mobile networks and its evolution path to the ultimate intelligence and autonomy. Although this paper proposes intelligent grading for mobile networks, the conclusion is also applicable to the definition of general network intelligence. The current thinking is still relatively elementary. We welcome contribution and discussion from academic community and industrial organization to further improve the taxonomy of the intelligence grading for mobile networks.

References

  1. You X H, Pan Z W, Gao X Q, et al. The 5G mobile communication: The development trends and its emerging key techniques. Sci Sin Inform, 2014, 44 (5): 551-563 [. 5G. 2014, 44 (5):551-563
  2. Wang T, Wen C K, Wang H, et al. Deep learning for wireless physical layer: Opportunities and challenges[J]. China Communications, 2017, 14(11): 92-111.
  3. Tang P, Li F, Zhou W, et al. Efficient auto-scaling approach in the telco cloud using self-learning algorithm[C]//Global Communications Conference (GLOBECOM), 2015 IEEE. IEEE, 2015: 1-6.
  4. Zhou W. TCRM: Telco Cloud Resource Management Using Real-Time Data Analysis[C]//Cloud Computing Technology and Science (CloudCom), 2016 IEEE International Conference on. IEEE, 2016: 480-481.
  5. O’Shea T, Hoydis J. An introduction to deep learning for the physical layer [J]. IEEE Transactions on Cognitive Communications and Networking, 2017, 3(4): 563-575.
  6. 5G Americas white paper. 5G Network Transformation. http://www.5gamericas.org/files/3815/1310/3919/5G_Network_Transformation_Final.pdf
  7. SELFNET. Framework for Self-Organized Network Management in Virtualized and Software Defined Networks. [Online]. Available: https://5g-ppp.eu/selfnet/
  8. CogNet. Building an Intelligent System of Insights and Action for 5G Network Management. [Online]. Available: https://5g-ppp.eu/cognet/
  9. SESAME. Small cEllS coordinAtion for Multi-tenancy and Edge services. [Online]. Available: https://5g-ppp.eu/sesame/
  10. ETSI ENI White paper, Improved operator experience through Experiential Networked Intelligence, https://www.etsi.org/images/files/ETSIWhitePapers/etsi_wp22_ENI_FINAL.pdf
  11. ETSI ZSM ISG White paper, Zero-touch Network and Service Management, https://portal.etsi.org/TBSiteMap/ZSM/OperatorWhitePaper
  12. ITU-T FG ML5G ToRs, https://www.itu.int/en/ITU-T/focusgroups/ml5g/Documents/FG-ML5G_ToRs.docx
  13. 3GPP Study of Enablers for Network Automation for 5G (Release 16), 3GPP TR 23791
  14. 3GPP New Study Item Proposal, RAN Centric Data Collection and Utilization, RP-181456
  15. 3GPP, “Technical Specification Group Services and System Aspects; Telecommunication Management; Self-Organizing Networks (SON); Concepts and requirements (Release 13),” 3GPP, TS 32.500, v13.0.0,
  16. N. Samuel, T. Diskin, and A. Wiesel, "Deep MIMO detection," IEEE 18th Int. Workshop Signal Process. Advances Wireless Communication (SPAWC), pp. 690–694, 2017.
  17. https://www.sae.org/standards/content/j3016_201401/preview/

 

YanWangYan Wang received his Ph.D. degree from the department of Electronic Engineering, Shanghai Jiao Tong University, China, in 2009.  He is a principal engineer at Huawei Technologies in Shanghai, China. He has led many research projects on the mobile network evolution, including EPC enhancement, NFV, SDN, CU separation, service chaining, MEC mobility, Mobile LAN etc. Many of the research outputs have contributed to the 3GPP 4G and 5G standards. He is now leading a team of future network evolution research and prototype, and he is also the leader of architecture group of Wireless Artificial Intelligence Alliance. His current interesting of study includes intelligent mobile network enabled by big data and Artificial Intelligence, as well as mobile deterministic networks

 

 

HuaHuangHua Huang is the director of Huawei Mobile Broadband Network Research Department. He is also the chief expert of the wireless architecture research in Huawei. Mr. Hua graduated from Zhejiang University with a Master degree in telecom science in June 1996 and joined Huawei in 2000. From 2000 till 2003, Hua is a senior engineer in Huawei 3G product, responsible for the system design of packet domain equipment. From September 2003 till April 2006, Mr. Hua was the leader of Huawei 3GPP SA2 Standard team and responsible for the system and architecture evolution standard research. He also joins the 3GPP TSG-SA plenary as delegate. Since 2006, Mr. Hua was responsible for Huawei wireless research as a director in access network and architecture areas. His current research is focused on the future wireless architecture, including AI, Big data, SDN, NFV, Cloud Computing, Open source, and other technologies, he is in charge of 5G architecture researches, and also lead the wireless AI research team in Huawei. 

 

Yingzhe LiYingzhe Li received the Ph.D. degree from Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, China, in 2011. He is a principal engineer at Huawei Technologies in Shanghai, China. He has research on the algorithm of Self-Organizing Networks, such as network optimization, Mobility robust optimization. Now He focuses on the applications of Artificial Intelligence in wireless networks.

 

 

 

Wei ZhouWei Zhou (M’15) received his Ph.D. degree from the department of Electrical Engineering and Information Systems, University of Science and Technology of China, in 2009. Currently, he is a Principal Engineer in Huawei Technologies Co., Ltd. His research interests include wireless communication, wireless Intelligence, machine learning and big data in next generation mobile network.

 

 

 

chih lin I croppedChih-Lin I received her Ph.D. degree in electrical engineering from Stanford University. She has been working at multiple world-class companies and research institutes leading the R&D, including AT&T Bell Labs; Director of AT&T HQ, Director of ITRI Taiwan, and VPGD of ASTRI Hong Kong. She received the IEEE Trans. COM Stephen Rice Best Paper Award, is a winner of the CCCP National 1000 Talent Program, and has won the 2015 Industrial Innovation Award of IEEE Communication Society for Leadership and Innovation in Next-Generation Cellular Wireless Networks.

In 2011, she joined China Mobile as its Chief Scientist of wireless technologies, established the Green Communications Research Center, and launched the 5G Key Technologies R&D. She is spearheading major initiatives including 5G, C-RAN, high energy efficiency system architectures, technologies and devices; and green energy. She was an Area Editor of IEEE/ACM Trans. NET, an elected Board Member of IEEE ComSoc, Chair of the ComSoc Meetings and Conferences Board, and Founding Chair of the IEEE WCNC Steering Committee.

She was a Professor at NCTU, an Adjunct Professor at NTU, and an Adjunct Professor at BUPT. She is the Chair of FuTURE 5G SG, an Executive Board Member of GreenTouch, a Network Operator Council Founding Member of ETSI NFV, a Steering Board Member and Vice Chair of WWRF, a Steering Committee member and the Publication Chair of IEEE 5G Initiative, a member of IEEE ComSoc SDB, SPC, and CSCN-SC, and a Scientific Advisory Board Member of Singapore NRF. Her current research interests center around “From Green & Soft, to Open & Smart”.

 

Qi SunQi Sun received the B.S.E. and Ph.D. degree in information and communication engineering from Beijing University of Posts and Telecommunications in 2009 and 2014, respectively. After graduation, she joined the Green Communication Research Center of the China Mobile Research Institute. She has published over 20 conference and journal papers and over 20 patent applications. Her research focuses on 5G and 5G+ technologies, including wireless big data driven intelligent RAN optimization, network resource management, non-orthogonal multiple access, etc.

 

 

siming zhang croppedSiming Zhang received her Ph.D. degree in wireless communications from the University of Bristol (UK) in 2016. She currently works at the Green Communications Research Center in China Mobile Research Institute (Beijing). She is one of the co-leads on NGMN Trial and Testing Initiative. She is also co-leading WG1 of the Wireless Artificial Intelligence Alliance. She is the Associate Managing Editor of the IEEE 5G Tech Focus Journal. Her current research interests include PHY layer design on Massive MIMO and mmWave, especially on channel modeling and prototyping, wireless big data analysis, and AI application in the RAN domain.

 

Editor: Panagiotis Demestichas  

 

Christian James Aguilar-Armenta, Federal Telecommunications Institute of Mexico, This email address is being protected from spambots. You need JavaScript enabled to view it.

IEEE Future Networks Tech Focus: Volume 2, Number 3, December 2018 

Abstract

Because the new digital ecosystem implies the development of novel telecommunications services, both telecom operators and regulators are faced with new opportunities and challenges. It is thus necessary to understand the role that telecom operators will play in the value chain of new business models, their interactions with other stakeholders, as well as the potential regulatory impacts of all this. In this article we present an examination of some key innovative business models wherein operators are the main actors. Our data analysis leads us to conclusions about regulatory challenges and potential competition implications for 5G.

1. Introduction

In this article we examine how telecom operators (Telcos) and stakeholders are currently engaging in new business models fostered by the new digital ecosystem when providing services, so as to generate hypothesis about the competition impacts and regulatory implications that these might bring in years to come. We begin by presenting an analysis of the participation that Telcos have in current new business. Subsequently, we address the potential repercussions and challenges that these new business models represent for both operators and regulators.

2. New Digital Ecosystem

The new ecosystem has the potential to lure in both novel and extant participants of the digital world, increasing the participation of the latter with new services and innovative technologies in Telcos' value chain.

But, all in all, what makes this new ecosystem so attractive and promising? The most likely answer is the technologies that are essential for the development of 5G, which will bring different attributes compared to the current 4G networks, namely: massive MIMO, beam-forming, Software Defined Networking (SDN) and Network Function Virtualization (NFV). The last two in particular will allow the Network Slicing to meet specific needs with specific network attributes [1]. This capacity creates a number of possible services that we have not seen so far. These technologies, in combination with the massive deployment of small cells, will allow 5G networks to have the capacity to: 1) provide higher speed and broadband (xMBB); 2) support the massive connectivity of various devices (mMTC); and 3) provide connectivity with very low latency and with high level of reliability (uMTC) [1], the latter commonly known as URLLC.

This new digital ecosystem will allow the development not only of super connectivity services but it will also offer specific solutions across different sectors in both urban and rural areas. However, these opportunities will not only be presented to Telcos but to all the stakeholders involved in the ecosystem. It is right here where the new disruptive business models emerge and where the value chain will be modified due to the participation of more actors.

3. Methodology

A systematic review of a wide variety of sources was performed, aiming to pinpoint: 1) new business models for Telcos; 2) the competition implications; and 3) regulatory challenges. We focused on academic and telecom standardization bodies’ databases, Telcos’ official websites, informative, analytic or editorial texts published online by consulting agencies, as well as specialized news sites. Although the last three cannot be considered as scientific references, sometimes they were the only existing sources of information about newest business models. In order to narrow down our search to pertinent resources, we developed a boolean search combination of terms related to the new digital ecosystem (e.g. IoT, 5G, Big Data, AI, etc.), plus those pertaining to Telcos (e.g. network operators, communications service providers, etc.).

 4. Results

So far, there is no launch of a large-scale 5G network in any country, there has only been pre-commercial testing of 5G services [2]. In spite of this, at present there are services of the new digital ecosystem that enable the development of new business models of which Telcos are the main providers.

We were able to identify 27 use cases in which operators are the main actors. It is important to note that the number of cases are not of primordial importance for this article. What is indeed substantive is the identification of new business models and, above all, the possibility to predict the potential competition and regulatory implications for the sector. Table 1, therefore, concentrates on only five representative use cases that are outside the universe of services that Telcos traditionally offer, in order to synthesize the most relevant information about these new business models. In the subsequent section, however, we expound on the competition and regulatory aspects that we consider require attention from the regulators, based on the analysis of the 27 use cases that we identified.

As a supplement to the results, Fig. 1 features a schematic of the 27 use cases that we identified, classified by groups, showing the sectors of greatest commercial interest for Telcos that they target [3]. This schematic is not a standard representation of the new business models that exist in the market today; it only shows the particular distribution of the use cases that we found. 

Figure 1 Schematic representation of services by group and target sectors

Figure 1. Schematic representation of services by group and target sectors

 

Table 1. Selection of use cases

Use cases Service Network Requirements Business model
 

IoT [4]

Mobile network platform that provides connectivity, management and control for autonomous vehicles in South Korea. Low latency;
High reliability;
High throughput;
High availability;
Connection density;
Traffic volume density;
Coverage (mobility);
Security;
Data analytics;
AI.
Marketing of the 5G self-driving technologies consisting in network capacity (28 GHz band), AI, sensors, and 3D HD maps by SK Telecom to provide connectivity, management and control of autonomous vehicles. SK Telecom in partnership with Nvidia and LG Electronics, as well as in collaboration with The Korean Transportation Safety Authority and the University of Seoul.
Big Data [5] Telefonica’s Big Data service unit to provide an information analysis service to help its clients in decision-making and resource management processes.  Data analytics;

Cloud computing;
Security;
Coverage.

Marketing of three Big Data business lines: 1) Business Insights: provides companies with anonymous and aggregated data collected by Telefonica’s networks; 2) Synergic Partners: provides analytical and consultancy data services; and 3) Big Data as a service: giving enterprises the means to make better use of their own data, using Telefonica’s cloud infrastructure.
Blockchain [6] City Pass to pay multiple services such as bike sharing, tourist sites, libraries, etc. by means of a card or a mobile app that allows authentication and secure mapped direct transactions with the platform. Low latency;

High availability;
Coverage;
High reliability;
High security;
High throughput;
Connection density;
Cloud computing;
Data analytics;
AI. 

Marketing of the City Pass service by Deutsche Telekom to carry out direct, secure and decentralized digital transactions between the user and the platform. Open and independent system for the integration of more services.
AI [7] Home device (speaker) based on voice recognition that works as a virtual assistant for smart home control services, music, weather and traffic information, e-commerce service and multimedia playback. Cloud computing;

Data analytics;
AI;
Machine learning;
Availability;
Security;
Connection density; Interoperability.

Marketing of the virtual assistance device by SK Telecom, with an open interface to incorporate other AI devices and services into the ecosystem. SK Telecom will also integrate other AI developers to strengthen the ecosystem.
 

Media & Entertainment

[8]
Test of 5G commercial network in the Winter Olympic Games in South Korea to provide services such as: 1) 4K transmission and 360° vision; 2) VR; 3) massive connectivity of devices and control with Edge Computing; 4) high throughput; and 5) enhanced broadband. High throughput;

High reliability;
Broadband;
Low latency;
Traffic volume density;
Connection density;
Coverage;
Quality;
Availability;
Security;
Spectral and energy efficiency;
Edge computing;
Cloud computing;
AI/VR/AR; MIMO and Beam-forming.

 Marketing of 5G services by KT, Intel, Toyota, Samsung and Ericsson. In particular, Intel provides the FlexRAN platform and Edge Computing technology, as well as cloud computing and data center functions; Samsung and Ericsson, in direct collaboration with KT, provide the 5G network in the 28 GHz band. The network test paves the way for the operator to launch commercial 5G services.

5. Regulatory Challenges and Potential Competition Implications

We seek to help regulators anticipate the needs to encourage investment by Telcos towards 5G success. In the following subsections we present a brief explanation of each of the aspects that we consider are of relevance to the authorities.

A. Regulatory Challenges

Spectrum. The new digital ecosystem requires spectrum classified into three general frequency ranges: <1 GHz, 1 - 6 GHz, and > 6GHz. The 600, 700, 800 and 900 MHz bands are important for the range below 1 GHz. The 3.4 - 4.2 GHz band is significant in the segment 1 - 6 GHz. Likewise, bands 1.4 and 2.5 GHz are important in this segment. The 26 and 28 GHz bands stand out among the frequencies above 6 GHz; however, there are others frequencies that are in the process of identification by the WRC-19 (i.e. 37- 43.5 GHz, 45.5 - 50.2 GHz, 66 - 76 GHz and 81 - 86 GHz).

It is also necessary to explore adequate models for assignment spectrum for both isolated areas (e.g. smart farming) and confined or delimited areas (e.g. smart factory) that require specific solutions. The use of unlicensed bands may not be sufficient for services that require greater security and reliability. We also consider it important to capitalize more from the secondary licenses of the spectrum, as well as from other spectrum sharing techniques at high frequencies that experience limited propagation and less interference.

Infrastructure. It is necessary to create adequate models for infrastructure sharing, mainly for indoor places in urban areas for small cell densification. Flexibility of new entrants who intend to market their infrastructure and spectrum could be another important factor.

Open architectures. In order to guarantee the interconnection and interoperability between networks, it is essential for regulators to be very attentive to the use of new technologies and to ensure both technological neutrality and the adoption of international standards among operators.

Quality of services. Quality standards will have to broaden their scope and not to be limited to throughput parameters.  Several services would require clear quality standards, particularly those that require low latency, connection density, traffic volume density, high availability and reliability.

Service differentiation. In theory, Network Slicing will allow a Telco to provide various services with specific requirements through the same network without interfering the traffic and performance of the different services that are on the network. If so, regulators would have to establish clear rules to avoid violating the net neutrality, similar to the following: 1) the capacity of the network should be sufficient to provide specialized services in addition to any Internet Access Service (IAS) that is provided; 2) the specialized services are not used or offered as a replacement for the IAS; and 3) the specialized services should not diminish the availability or the quality of the IAS.

Ethical and legal regulation of AI systems. Security and privacy are the aspects that most concern people when using AI systems, particularly due to the lack of a clear ethical and legal scenario that limits the scope and responsibilities of these systems.

Delimitation of responsibilities. It is important to define the responsibilities of the participants in the value chain, as well as to be able to identify the responsible (e.g. AI systems).

Security, privacy and data protection. It is evident that digital data is the sine qua non of all digital services. Therefore, regulators should encourage the development of the new ecosystem while ensuring: 1) privacy or control over the dissemination of people‘s personal data; 2) the non-vulnerability of the data; and 3) protection through some type of security.

B. Competition implications

Association or vertical integration. The associations of Telcos with other stakeholders in the provision of a service turned out to be an option for most of the new business models that we found.

Possible entry barriers. The associations can generate entry barriers for other operators and suppliers of technology, equipment, platforms or applications.

Tariff differentiation per service. The lack of a clear tariff plan for new services that involve the connection of several devices could affect the balance between the cost-benefit that users acquired and the costs that the operators need to recover.

Possible distortion of neutrality to competition. It is essential that the participation of the Government does not generate distortions to the market because of its power over public property.

Possible barriers to access essential supplies. Big Data services use aggregated and anonymous data collected from Telcos’ network users as essential input. This could generate commercial disadvantages to other competitors if there is an incumbent providing these services.

6. Conclusions

The new digital ecosystem represents several significant changes in the creation, provision and commercialization of new telecommunications services. From our analysis of the new business models identified, we derive the following conclusions:

  1. The new business models are aimed at vertical industries, specific sectors and business niches that go beyond connectivity services;
  2.  The new ecosystem represents business opportunities for all stakeholders of the digital world;
  3. The value chain can be modified upstream with the participation of new stakeholders that provide, for example, infrastructure for small cells, as well as downstream with the participation of intermedaries that offer services directly to the end user;
  4. In a very general way we consider that there are four possible participation scenarios for Telcos in the value chains: 1) they dominate the entire value chain and are responsible for providing both technology and services to end users; 2) they are the main actors of the value chain and are responsible for providing the service to end users; however, they require third-party specialists in the sector for the provision of technology and platforms; 3) they have the best technological network capabilities to support the specific requirements across different sectors, nonetheless they are not the ones who provide services to end users but intermediaries who know the sectors very well; and 4) they remain outside the value chain because technology developers, in collaboration with new specialists in the sector, have the ability to provide specific services to end users;
  5. The success of Telcos depends, among other things, on their ability to meet the specific requirements of users, the investment they make in their networks for the deployment of new capabilities, their strategy of participation in the value chain, and of their capacity to take advantage of all the wireless, fixed and satellite technologies that currently exist for the provision of mixed connectivity.

References

  1. A. Osseiran, J. F. Monserrat, and P. Marsch. “5G mobile and wireless communications technology”. Cambridge, UK: Cambridge University Press, 2016.
  2. D. Johnson, “5G Poised for Commercial Rollout by 2020”, IEEE Spectrum: Technology, Engineering, and Science News, 2018.
  3. K. Taga, R. Swinford, and G. Peres, “5G deployment models are crystallizing”, Arthur D Little, 2017.
  4. J. P. Tomás, “South Korea allows KT to test self-driving bus in Seoul”, Enterprise IoT Insights, 2018.
  5. J. P. Tomás, “Telefonica launches big data services unit”, Enterprise IoT Insights, 2016.
  6. C. Sentürk, and A. Ebeling, “City Pass – Blockchain”, Deutsche Telekom, 2018.
  7. J. P. Tomás, “SK Telecom unveils artificial intelligence service”, Enterprise IoT Insights, 2016.
  8. M. Dano. “KT’s millimeter wave 5G network transmitted 3,800 TB of data during Winter Olympics”, FierceWireless, 2018.

 

 

AguilarChristian James Aguilar A. received his Ph.D. degree in Electronic Engineering from the University of York, UK. He holds a BSc in Telecommunications Engineering from the National Autonomous University of Mexico (UNAM). He has more than five years of experience in the Telecom industry, public sector and research. Currently his is a researcher at the Federal Telecommunications Institute (IFT) of Mexico. Previously, he was the technical adviser of former Commissioner Adriana Labardini of IFT, a post he held for over three years. His latest research project revolves around disruptive business models for Telcos in the 5G ecosystem. He has published various scientific articles, among which stands one entitled: ‘Cantilever RF-MEMS for Monolithic Integration with Phased Array Antennas on a PCB’.

 

Editor:

Siming Zhang received the dual BEng degrees with the highest Hons. from the University of Liverpool (UK) and Xi’an JiaoTong and Liverpool University (XJTLU, China) respectively in 2011. She obtained her M.Sc with distinction and her Ph.D. degree from the University of Bristol (UK) in 2012 and 2016. She then joined China Mobile Research Institute and currently works on research areas ranging from Massive MIMO and mmWave, channel measurements and modeling, conductive testing and prototype development. She has been an active member of the IEEE Communications Society and IEEE Young Professionals. She serves as the Associate Managing Editor of the IEEE 5G Tech Focus. She is the co-lead on the PoC project in the NGMN Trial and Testing Initiative. She is the TPC for IEEE ISCC2017. She has received numerous awards for her outstanding achievements during her study and her career.

 

The IEEE 5G Intiative is looking to create robust cross-society working groups to support the initiative. If you are interested in participating, please fill out the contact form below, and we will communicate your information to the appropriate working group.

  • Web Portal / Content Development 
  • Publications 
  • Education 
  • Community Development 
  • Brand Development 
  • Technology Roadmap: Based on horizon scanning, interviews and expert knowledge, the mission of the 5G Roadmap working group is to identify short (~3 years), mid-term (~5 years) and long-term (~10 years) research, innovation and technology trends in the communications ecosystem. This will enable the development of a concrete innovation and engagement roadmap guiding the IEEE community towards maximum impact contributions across its societies, and in conjunction with its demand-side as well as the wider industry & standards ecosystem. The outcome shall be a live document with a clear set of (accountable) recommendations; the document shall be updated annually and be developed in conjunction with the other working groups.
  • Standards 
  • Industry Engagement: Seeking engagements from industry particularly with verticals that can be enabled or improved with 5G technologies. Industry partners with a understanding of 5G infrastructure can assist in explaining the core technology but central is industry contributions to verticals that will benefit from 5G implementations.
  • Conferences / Events 

Join the IEEE 5G Technical Community to stay informated about the activities occurring througout the IEEE 5G Initiative. 

Please fill out the following form completely  to indicate interest in joining the 5G Initiative: 

* required field

Select Working Group Interest(s)*Please select at least one.


If you have further questions please email the 5G Initiative co-chairs Tim Lee (This email address is being protected from spambots. You need JavaScript enabled to view it.) or Gerhard Fettweis (This email address is being protected from spambots. You need JavaScript enabled to view it.) with a copy to Theresa Cavrak, IEEE staff, (This email address is being protected from spambots. You need JavaScript enabled to view it.) to join any of the following working groups:

2022 Edition INGR Chapters and Abstracts

Full chapters of the INGR are available exclusively to signed-in participants of the IEEE Future Networks Initiative (FNI). Learn more and become a participant here today!

The INGR Executive Summary and Chapter Abstracts are FREE.

INGR Cover Exec Summary 2022

Executive Summary

INGR Cover Apps Svcs 2022

Full Chapter / Free Abstract

INGR Cover AIML 2022

Full Chapter /  Free Abstract

INGR Cover CTU 2022

Full ChapterFree Abstract

INGR Cover Deployment 2022

Full Chapter / Free Abstract 

INGR Cover Edge 2022

Full ChapterFree Abstract

INGR Cover Energy Efficiency 2022

Full ChapterFree Abstract

INGR Cover Massive MIMO 2022

Full ChapterFree Abstract 

INGR Cover mmWave 2022

Full ChapterFree Abstract 

INGR Cover Optics 2022

Full ChapterFree Abstract

INGR Cover Satellite 2022

Full ChapterFree Abstract

INGR Cover Security 2022

Full ChapterFree Abstract

INGR Cover SBB 2022

Full ChapterFree Abstract

INGR Cover SysOpt 2022

Full ChapterFree Abstract

INGR Cover Testbed 2022

Full ChapterFree Abstract 

 

 

Accessing the INGR Documents

Full chapters of the INGR are available exclusively to signed-in participants of the IEEE Future Networks Initiative (FNI). Learn more and become a participant here today!

The INGR Executive Summary and Chapter Abstracts are FREE.

Becoming a participant of the IEEE Future Networks Technical Community is:

  • free for all IEEE society members
  • $5 for IEEE student members
  • $10 for IEEE members who are not members of a society, and 
  • $15 for non-IEEE members.

JoinTCbutton

 


Click HERE
 to become a participant and unlock access to the INGR and the many benefits
of the IEEE Future Networks community. Instructions for joining the IEEE Future Networks community are available.

 

INGR Webinar Series Videos

Video promo screenshot

The INGR working groups have presented a series of monthly webinars. Watch the videos today!

 

INGR 2021Ed Banner

About the IEEE International Network Generations Roadmap (INGR) 

The purpose of the International Network Generations Roadmap (INGR) is to stimulate an industry-wide dialogue to address the many facets and challenges of the development and deployment of 5G in a well-coordinated and comprehensive manner, while also looking beyond 5G by laying out a technology roadmap with 3-year, 5-year, and 10-year horizon. Future network technologies (5G, 6G, etc.) are expected to enable fundamentally new applications that will transform the way humanity lives, works, and engages with its environment. INGR, created by experts across industry, government and academia, is designed to help guide operators, regulators, manufacturers, researchers, and other interested parties involved in developing these new communication technology ecosystems. Each chapter is peer-reviewed by internal industry experts, then sent out for external review by subject matter experts, and finally receives an editorial review before being finalized for publication.

Development of the INGR has produced a technical community that fosters the exchange of ideas, sharing of research, setting of standards, and identification, development, and maturation of system drivers, system specifications, use cases, and supported applications. As work continues, new experts are encouraged to participate, to evolve and strengthen this crucial document. Join us! 

Please send a message to one of the groups listed below to express your interest in participating. 

Be notified of new releases and all other Future Networks Initiative news by joining our technical community

JoinTCbutton 

 

 

 

2021 Edition INGR Chapters

INGR Resources:

1st Edition INGR Chapters 

 

 White Papers

 
 

Industry Forum Presenations | October 2020

 

Systems Optimization Workshop - January 2021


Reports

INGR Presentations


INGRrecruitmentBanner

 

 

 

 

 

 

Working Group Teams

Working Group  Chairs  Email to contact to participate 
Co-Chairs This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it.This email address is being protected from spambots. You need JavaScript enabled to view it. This email address is being protected from spambots. You need JavaScript enabled to view it. 
Applications and Services This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it. This email address is being protected from spambots. You need JavaScript enabled to view it. 
Artificial Intelligence / Machine Learning  This email address is being protected from spambots. You need JavaScript enabled to view it.This email address is being protected from spambots. You need JavaScript enabled to view it. This email address is being protected from spambots. You need JavaScript enabled to view it.
Connecting the Unconnected  Sudhir Dixit, This email address is being protected from spambots. You need JavaScript enabled to view it. This email address is being protected from spambots. You need JavaScript enabled to view it.
Deployment This email address is being protected from spambots. You need JavaScript enabled to view it.,This email address is being protected from spambots. You need JavaScript enabled to view it. This email address is being protected from spambots. You need JavaScript enabled to view it. 
Edge Services and Automation This email address is being protected from spambots. You need JavaScript enabled to view it.This email address is being protected from spambots. You need JavaScript enabled to view it.,  This email address is being protected from spambots. You need JavaScript enabled to view it. This email address is being protected from spambots. You need JavaScript enabled to view it. 
Energy Efficiency This email address is being protected from spambots. You need JavaScript enabled to view it.This email address is being protected from spambots. You need JavaScript enabled to view it. This email address is being protected from spambots. You need JavaScript enabled to view it.
Massive MIMO This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it. This email address is being protected from spambots. You need JavaScript enabled to view it. 
Millimeter Wave and Signal Processing  This email address is being protected from spambots. You need JavaScript enabled to view it. This email address is being protected from spambots. You need JavaScript enabled to view it. 
Optics This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it. This email address is being protected from spambots. You need JavaScript enabled to view it. 
Satellite  This email address is being protected from spambots. You need JavaScript enabled to view it.This email address is being protected from spambots. You need JavaScript enabled to view it. This email address is being protected from spambots. You need JavaScript enabled to view it.  
Security and Privacy This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it. This email address is being protected from spambots. You need JavaScript enabled to view it.  
Standardization Building Blocks  This email address is being protected from spambots. You need JavaScript enabled to view it.This email address is being protected from spambots. You need JavaScript enabled to view it.This email address is being protected from spambots. You need JavaScript enabled to view it.
This email address is being protected from spambots. You need JavaScript enabled to view it.
Systems Optimization This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it. This email address is being protected from spambots. You need JavaScript enabled to view it.  
Testbed This email address is being protected from spambots. You need JavaScript enabled to view it.This email address is being protected from spambots. You need JavaScript enabled to view it. This email address is being protected from spambots. You need JavaScript enabled to view it.  

 

The IEEE Future Networks Initiative is in the process of creating an Industry Consortium for the broad array of companies working in the 5G and future networks space. We recognize that amid a global financial crisis caused by the COVID-19 pandemic, innovation projects are often dropped for cost-saving reasons. Our goal is to develop a program that brings value to individual member companies while encouraging and facilitating reduced-cost innovation that is fostered by collaboration.Testbed Concept Icon

Membership in the program will provide participating companies with the following:

  • Collaborative online testbed – a virtual testing platform that emulates the network from end to end. Member companies can use the platform for their own development and have the opportunity to collaborate with other companies to test together for conformity, compliance, proof of concept, validation, etc.
  • Educational services – FNIC will create an Industry Expert Speaker roster from the worldwide IEEE community and work with member companies to address staff educational needs by providing lectures and presentations.
  • INGR roadmap access – The International Network Generations Roadmap project will create a special Industry Advisory Board for companies that participate in the FNIC. The FNIC IAB will get exclusive bi-annual presentations from INGR leadership, a copy of every INGR release to distribute internally, and the ability to place staffers in INGR working groups.
  • Working groups and events – member companies can create or participate in working groups dedicated to 5G/6G verticals within the IEEE ecosystem. These working groups can develop events – workshops, expositions, hackathons, etc. – that address industry needs in specific verticals and are managed via the expertise of the IEEE Events team.

For more information or to schedule a call for a more detailed presentation, send an email to This email address is being protected from spambots. You need JavaScript enabled to view it..

 

-Anwer Al-Dulaimi, Future Networks Industry Consortium Chair
-Ashutosh Dutta, Future Networks Initiative Co-Chair