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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.

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{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

IEEE5G AlexWyglinksi TwitterGraphicIEEE 5G Transmissions: Podcasts with the Experts
An IEEE Future Directions Digital Studios Production 

In this episode of IEEE 5G Transmissions: Podcasts with the ExpertsAlex Wyglinksi speaks to how 5G can be used with cognitive radio and vehicular dynamic spectrum access to support large-scale wireless communications between autonomous vehicles. Alex is co-chair, IEEE 5G Community Development Working Group, president of the IEEE Vehicular Technology Society and a full Professor of Electrical and Computer Engineering at Worcester Polytechnic Institute.

 

 

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An IEEE Future Directions Digital Studios Production 

5G Connectivity Beyond the City – Agricultural use cases through 5G RuralFirst

In this episode, James Irvine talks with Karina Maksimiuk and Greig Paul about their work with 5G RuralFirst, a UK government testbed and trial project, which describes itself as a ‘call to action’ to be sure the benefits of 5G go beyond the city.

 

  • Dr. James Irvine is Reader and Head of Mobile Group in the Department of Electronic & Electrical Engineering, Strathclyde University, and co-chair of the IEEE Future Networks Community Development Working Group,
  • Karina Maksimiuk is CEO of Uncorde and representative for Agri-EPI Centre with 5G RuralFirst where she is a work package lead for AgriTech Use Cases,
  • Dr. Greig Paul is Lead Mobile Networks & Security Engineer in the Department of Electronic & Electrical Engineering with Strathclyde University.

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Darpa Challenge Podcast ImageIEEE 5G Transmissions: Podcasts with the Experts
An IEEE Future Directions Digital Studios Production 

Do We Still Need the FCC? Darpa's Spectrum Collaboration Challenge

In this episode, Paul Tilghman, DARPA program manager, speaks to the three-year-long Spectrum Collaboration Challenge that attempts to answer the question, Do we still need the FCC? DARPA, the United States Defense Advanced Research Projects Agency, gamified a system to handle Dynamic Spectrum Sharing through the creation of SDN radios using the power of artificial intelligence and collaborative autonomy to navigate, share and optimize wireless spectrum in a testbed called Colosseum, and invited the world to compete. The live championship event takes place on October 23 at Mobile World Congress LA, and will be live-streamed.

 

Subject Matter Expert

  • Paul Tilghman, program manager in the Microsystems Technology Office, Defense Advanced Research Projects Agency, USA

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IEEE 5G Transmissions: Podcasts with the Experts
An IEEE Future Directions Digital Studios Production 

The International Network Generations Roadmap – Executive Overview

The International Network Generations Roadmap (INGR) is stimulating 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. 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. The INGR, created by experts across industry, government, and academia, helps guide operators, regulators, manufacturers, researchers, and others involved in developing new communication technology ecosystems by laying out a technology roadmap with 3-year, 5-year, and 10-year horizons. 

Subject Matter Experts

  • Narendra Mangra
    Co-chair, International Network Generations Roadmap
    Principal, GlobeNet, LLC

  • Rose Qingyang Hu, PhD
    Co-chair, International Network Generations Roadmap
    Professor of Electrical and Computer Engineering, Associate Dean for Research, College of Engineering, Utah State University

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Podcast Transcript 

Brian Walker:  Welcome to the IEEE Future Networks Podcast Series, Podcast with the Experts, an IEEE Future Directions Digital Studio Production. In this episode, Co-chairs Narendra Mangra and Rose Hu discuss the International Network Generations Roadmap, commonly referred to as the INGR. Narendra is a Principal at GlobeNet, LLC, and Rose is a Professor of Electrical and Computer Engineering and an Associate Dean for Research for the College of Engineering at Utah State University. The INGR is stimulating an industry-wide dialog to address the many facets and challenges of the development and deployment of 5G in a well-coordinated and comprehensive manner, but also looking beyond 5G. Rose and Narendra, thank you for taking some time to discuss the INGR with us today. Can you describe what IEEE Future Networks seeks to provide with the International Network Generations Roadmap?

Rose Hu:  I think the purpose of the International Network Generation Roadmap, or INGR, is to provide a good platform for the general community, the dialog, technical dialog to address the many ideas and challenges of the development and the deployment of 5G and beyond in the coordinative and the comprehensive manner.

Brian Walker:  Can you describe some of the challenges that the Roadmap takes on?

Narendra Mangra:  I think it's interesting for roadmaps to look at the different ways or paths forward. Not every path will lead to a productive solution. So, we want to look at some of the obstacles or problems along the way, and what needs to be solved, and so it's different groups that are looking at either solving problems or creating solutions or just looking at the overall passage points for the different paths.

Rose Hu:  Yeah, I think to add to what Narendra just said, I think the challenges itself actually comes from, for the Roadmap, to quickly identify new technologies in its migration and takes lots of expertise to do that, and also because the Roadmap, the scope of the Roadmap itself is quite large. It covers the system side, hardware, and application, technology from the interface all the way to the network, so I think how to group people together to work in the comprehensive and coordinative manner, I think itself is very challenging, but also very rewarding.

Brian Walker:  Great. Now who's contributing to the INGR?

Rose Hu:  I think it's generally, it is a community effort. So, the older techno community people from industry, government and academia should be able to identify areas and to work together and to get all the benefits from that.

Narendra Mangra:  Yeah, and then also I would say that the industry, government, and academia; they would have different focuses obviously, whether it'd be research and development or policy, those basic solutions. But we also have about 15 working groups. At least six of them are new that will be working in the second edition as well, that will have quite a diversity of different perspectives and provide a lot of benefits to the overall communities.

Brian Walker:  So, the INGR has 15 working groups covering a very broad range of technology and business and social issues. What can telecom industry manufacturers learn from this first edition?

Narendra Mangra:  The first edition actually lays the structure and foundation for subsequent editions. So, a lot of the work for the first edition actually was to fill the frameworks and the pattern of how we're going to move forward for a lot of the working groups. That at least has been part of the first edition, that will continue on with the second edition. New working groups, I suspect, will be doing a lot of the foundation building for the second edition. But that should help shape a lot of the different areas for development to move forward.

Rose Hu:  Yeah, to add to what has been said, I think because we have 15 working groups covering a very broad range of technology, business and social issues, I think from telecom manufacturers, or in general, from the telecom ecosystem perspective, different stakeholders should be able to identify and shape the divestment of key areas of interest and also get key input, or output, from what has been provided from this first edition to either provide guidelines or advise in each specific area from the whole ecosystem perspective. I think that actually is very important, because the whole system is designed from the future 5G and the 6G beyond is really that the efforts all together from the ecosystem perspective.

Brian Walker:  And what should, for example, an enterprise pay attention to?

Rose Hu:  I guess from enterprise perspective, it was the immediate sphere of the current focus. Because, so 5G can be viewed as a network of networks, and can drive evolutions in various ecosystems that result in shifting industry structure. And also, adjacent industry bond race. So that actually is very important perspective for enterprise to pay attention to.

Narendra Mangra:  Yeah, and to add to Rose's description, I mean, yes, for sure that is a very important area for shifting boundaries to see where a particular company or firm may fall, but also 5G moves well beyond just another extension of 4G with high-speed communications. There are a whole lot of different types of communications capabilities related to ultra-reliable low latency communications and Internet of Things as well that we need to factor in.

Brian Walker:  What would you like government representatives and regulators to learn from this Roadmap?

Narendra Mangra:  I think it is important for government representatives and regulators to look at and see what policies that can promote that would help implement some of the technologies that would provide benefits to society. And there are certain issues that should be sorted out, too, and are relevant later, especially as it relates to data security, privacy, and ethics.

Rose Hu:  Yeah, and also I think through the Roadmap I wish we can get more active engagement from government representatives and regulators, because the future of 5G and the 6G definitely, the key issue of that, for example, is spectrum. and everybody's talking about spectrum 5G and the 6G. That's actually itself, the research, development, and the deployment, that actually, both sides actually from Roadmap perspective, we can provide input, and from government representatives and regulators, they can provide lots of advice in the guideline as well.

Brian Walker:  With everything in telecom's network generations constantly evolving, how will the INGR effort address the constant change?

Rose Hu:  We actually considered this constantly evolving nature of telecommunications, that's from generation to generation. We actually have thought of defining Roadmap in both short-term and long-term perspective to address those different changes. Such as three-year, five-year, or ten-year scope so that we can provide a coordinated and a structured approach to new technologies and enablers for the advance of society.

Narendra Mangra:  To add to Rose's comments actually, it's important to see where these different technologies would lead towards. So, we want to see, I guess the philosophy, where it could possibly lead towards. But part of this effort is also to really look at this as more of structured approach, so we don't stumble along. And we're not saying that we can see the future to know where all these things will lead, but we want to address this in a more coordinated approach. As these different technologies evolve to see where we're going, and to be a guiding light with industries as possible.

Brian Walker:  The INGR is a very large effort with a very large effort with a lot of people involved. What's needed for it to be successful for the publics you want it to serve?

Narendra Mangra:  At a minimum, we want to be able to have enough foresight to identify and prioritize the different areas of interest. So, we welcome feedback from industry, government, and academia as well. And of course, we were always looking for volunteers for the 15 working groups we have.

Rose Hu:  Yeah, I think just to add to what Narendra said, yeah, we actually actively try to engage volunteers from different disciplines and different segments: industry, government, academia to get involved in both technical ways, but also help us to distill and a broad and a diverse perspective and the divestment into the general public. I think that actually is very important to help the success of this INGR effort.

Brian Walker:  And where can people go to learn more?

Rose Hu:  Again, we have various ways actually to promote or disseminate what we are doing. So, I think a good way for people to learn more is through our IEEE Future Network International Network Generation Roadmap web page. So, this webpage you can actually found-- it can be found online. And also, we have like whitepaper and also different working group delivery. So, all those actually provide a very good technical contents and also progress on what we are doing, to the general public.

Narendra Mangra: So, in addition to Rose's comments, they can go to Future Networks at IEEE.org at "slash" Roadmap. And we will be having periodic working group meetings throughout the year and they can find out more information on the progress of the chapters.

Rose Hu:  I think, yeah, that's a very good point. I have like something actually try to promote here as well, because associated with IEEE Network Initiative and this Roadmap, we have different workshops and conferences such as 5G Summit and 5G Forum and some other related workshops. So, we actually all welcome people to attend and we can learn tremendously the state of art of this Roadmap effort there.

Brian Walker:  So, we've covered a lot in this podcast, but is there anything else that you would like people to know?

Narendra Mangra:  We are just gearing up for the second edition of the INGR, and volunteers are welcome to reach out to the working groups at this point, so they can help steer as far as where are the contents and what are the main areas that we should be focusing on. And to provide feedback as well, too, as we move forward.

Rose Hu:  Yeah, and I don't have much to add. Just one thing I want to emphasize, because this is effort that actually from these different experts from industry, government, and academia, and also experts open to public. So, we do welcome and highly encourage volunteers to participate and to be part of this great effort.

 

 


IEEE Future Networks Podcasts with the Experts

An IEEE Future Directions Digital Studios Production 

The 5G Energy Gap – A Fatal Flaw for 5G Deployment?

As massive 5G networks are being deployed in full speed in 2020, there is a potentially fatal flaw lurking in the supporting infrastructure, the utility distribution network. The 5G Energy Gap describes the uncertain ability of the utility grid to meet load energy requirements of potentially billions of devices while maintaining grid reliability. The Internet of Things, Industrial Internet of Things, edge computing and other technologies and network trends increase the issue exponentially.

Energy Harvesting (EH) solutions can supplement or even mitigate the multitude of tiny power requirements of systems where it matters most, at the edge. Scavenging every form of physical, ambient energy from the surrounding environment, EH spares the utility grid and power plants, and is a critical factor in addressing the 5G Energy Gap,

View the INGR Energy Efficiency White Paper and other INGR Chapters

Subject Matter Expert

Brian Zahnstecher 
Chair, INGR Energy Efficiency Working Group
Principal, PowerRox 

 

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Podcast Transcript 

Brian Walker:  Welcome to the IEEE Future Networks Podcast Series, Podcast with the Experts, an IEEE Future Directions Digital Studio Production. In this episode, we hear from Brian Zahnstecher, Chair of the Energy Efficiency Working Group for the International Network Generations Roadmap, and Principle of PowerRox. Brian speaks to a potentially fatal flaw lurking in the infrastructure supporting 5G deployment, the utility distribution network. The 5G Energy Gap describes the uncertain ability of the utility grid to meet load energy requirements for potentially billions of devices while maintaining grid reliability. That said, Energy Harvesting solutions can supplement or even mitigate the multitude of tiny power requirements of systems where it matters the most at the Edge. Welcome, Brian. Thank you for taking time to contribute to the IEEE Future Networks Podcast Series. Can you start by telling us about the 5G Energy Gap issue, and why it's bringing a perfect storm to the juncture of telecoms and power?

Brian Zahnstecher:  What I observe is a disconnect in the industry, particularly as all these massive 5G networks are being deployed and the disconnect is that all these tiny power devices and things that are supposed to be out there, the many billions, or even the trillions things, have a much higher what are called power cost factor, or essentially what is a multiplier on how much energy needs to be generated to support all these Edge devices. And so even though a lot of these things are supposed to be micro-power devices that when you talk about a factor of five to six orders of magnitude of energy that has to be generated to support them, and then you multiply that by the sheer scale of these number of devices, that can be supported even by locally within a region or whatever, a base station or whatever you want to call it, then it starts to really add up to be a highly disproportional demand on the actual utility grid and the power plant to support the load of all these perceptively almost negligible amount of tiny power devices that are on the edge of the network. And that disconnect between the tiny amount of power that they're consuming, you know, battery powered devices and all at the Edge, versus how much has to be generated, is what I'm referring to as the 5G Energy Gap. Now there's also this question about the perfect storm, and where that really comes from is that -- well, one nice thing about this issue is that a potential solution, right, is to actually supplement tiny bits of power on these tiny devices at the edge. And so that's where Energy Harvesting comes in and that has been a major focus area of mine for the past six or so years. And that's been kind of seen as more of an emerging type of nascent technology. And because of that finding its justification in the mainstream and real applications has been challenging from both a technical fit and a cost benefit type of analysis. But now here with the 5G Energy Gap, we have a potential real issue and now all of a sudden Energy Harvesting is a great potential solution to that gap and that issue. And so that's why I say kind of the perfect storm is the deployment of 5G, the massive uptake of IoT, and IIoT, the Industrial Internet of Things, and all these wearables and wireless sensor networks and little doohickeys and whatnot, combined with energy harvesting, and that opportunity is what I actually refer to as the perfect storm, as I see it today.

Brian Walker:  So, would you say 5G is the first network deployment to impact the utility grids ability to meet load energy requirements while maintaining grid reliability?

 Brian Zahnstecher:  So, not directly to the best of my knowledge. So, if you're saying like, "Oh, did we see this with 4G LTE? Or 3G, or previous generations of deployments?" And I would say, no, because-- and the main reason is that nothing before was enabling just the sheer number of things, and that would be touching the edge of the network that the network had to support. So, you know, before we're talking at most-- we're talking like, you know, smartphones and things and especially in the 4G, you know, 3G to 4G LTE era, and their streaming requirements and whatnot. But not supporting a whole bunch of these tiny power things that other-- especially lower power networks may have been supporting before. So, they're even in the cellular space, there are things like LTE CAT-1, there's one called CAT-M, and NBIoT or Narrow-Band IoT, specific protocols to address from a data perspective to address all these low-power, low-bandwidth, high-latency devices. But we've never had an occasion or application or deployment of saying, ‘well, we could have 10,000 sensors or nodes or endpoints within a single spot, serviced by a single base station,’ and that's really the difference here. So, there's no, I would say, direct precedent for this Energy Gap that we talked about, but what I always see as a very good analogy is look at the deployment of PV, right, of photovoltaics and solar in Germany, I think we're talking maybe roughly ten years ago or so, where the government said, "We'll subsidize this, we really need to make a concerted effort to ensure there's high penetration of PV within the country and the consumers is a good thing to do." And what they found out is they put the stuff out there too fast, before the energy grid was able to handle it. So, in other words, you have a grid that was designed for unidirectional distribution and all of a sudden you're putting all these solar points at the end point that are now turning into a bidirectional grid to feed some of that stuff back. And because it got deployed so fast before they were really kind of prepared for it, or foresaw the impact of what happens with doing it too fast at scale on a grid that was not necessarily designed for it, is they saw massive rolling blackouts and all kinds of grid stability issues. So, to me, that's kind of the closest analogy and case study in something like this that we can learn from, and that there is precedence for. The only difference, and I believe the reason that the  connection between the two hasn't been widely recognized, is again to the point about now we're talking about tiny power devices that maybe at first blush seem like a relatively negligible amount of energy as a pool even collectively, but again, when you take that power cost factor multiplier into account, that's what makes it kind of a new animal in this regard.

Brian Walker:  Can you describe the flow of power from end-to-end, as well as the dynamics of the Energy Gap in 5G Deployment?

Brian Zahnstecher:  Sure. So, I like to think of everything in terms of sources and loads in terms of energy. And so, this sort of applies to something that I would refer to as the power value chain, which basically follows energy from generation all the way through to the end load. So, as an example, let's say in the 5G network, an example of the blocks in this chain would be starting from generation, which is the power plant. And it doesn't matter what it is, whether it's coal-fired plant, or a renewable one, or whatever, and then that outputs some energy, which goes through a utility distribution network, which makes its way into either buildings, or let's say in the example of your edge device, your smartphone or your little wireless sensor network thing, that's serviced by a base station. So, the energy flow goes through the utility grid into the base station, and through its power amplifier and other blocks, to then output a transmitted wireless signal from its output antenna, which is then received wirelessly by the device at the edge. And then, of course, consumed in that load. So, that is the end-to-end power value chain for, say, an edge device like that on a wireless network. Now, if we said, for instance, what about a server in a data center that maybe we wanted to characterize-- the load in that is like a CPU or a memory, or something in a system that sits in a data center that's ultimately consuming the energy. So, from there it's the same blocks in the front side, you know, goes from the power plant through distribution into the building, but now within the building, it may go through some energy storage, some backups, something like battery system, UPS, something like that, uninterruptible power supply, and then it goes to the IT equipment racks where that typically AC power or voltage is then converted by some kind of front end power supply or bulk power supply or AC to DC or rectifier or silver box, whatever buzzword people like to use, and that's converted into DC that's used by the system and then there's usually a series of DC to DC regulators that will then convert from higher voltages to lower voltages that are requirements for all the system loads in ASICS such as CPU and memory and FPGAs and things like that. So, and in that case there are more conversion stages, voltage regulation converter stages and therefore every stage of whether it's conversion or distribution or whatever has some loss associated with it, and so, that would be the power value chain to go from say a CPU on a server, back to the power plant.

Brian Walker:  So, do you believe the utility industry's evolution to a smarter grid will address this challenge?

Brian Zahnstecher:  Yeah, I think somewhat. Certainly, every day more hooks are put in there for enabling what I refer to as intelligent power management. So that means both hardware and software hooks are enabling features in equipment that allow not only the recording of information and telemetry data, that can be aggregated up all the way from that load level, from a subsystem level, so, say from a CPU or data storage or networking stuff within the server and to the system level, to know how much the piece of equipment or the radio unit or whatever is consuming, up to even say a last level, or a regional level, or data center level. And so, it's one thing to just feed that information back up kind of as a one-way telemetry indicator, but then the other great thing is now we can take that information, perform analytic analysis on it, and then we can use it to do all kinds of great things. And that can range from enabling say a business case analysis, for instance, maybe you just want to optimize your utilization based on the dynamic price of the real time energy markets, so, knowing how much you're consuming and where regionally you're consuming it may-- and knowing how the price of energy is changing in the near term may cause you to shift some of that load from one place another as really a cross optimization of OPEX, of operating expense. But you may also take some of those analytics and realize that you can more intelligently manage the power within systems, so, you feed that information back into the system to make decisions and changes in real time the way perhaps the power is allocated either within a building or a data center or even within a system, to make sure that you're optimally maximizing your energy efficiency from the system level all the way up to perhaps the regional level, if you're-- there's a lot of energy involved. 

Brian Walker:  You say there are critical points in the 5G network that are ideal for techniques to optimize energy use. Where are those points?

 

Brian Zahnstecher:  I usually look for the opportunities within the network to kind of break them down of all the network constituents and then see and prioritize them by what's the lowest hanging fruit. One, where can you have the most impact? And two, just which block in that network has the biggest impact to the global energy footprint, right? And so luckily, both those things happen to fall within the same block in the network, in my opinion, at this point, and that falls in the base stations. So, today, if you look at the global energy footprint for all ICT or telco or communication networks, however you want to refer to it, that full-power pie-- and that includes everything that touches the network from data centers to even UE’s, user equipment, which is essentially like your smartphone and everything on the edge, if you add all of that up, 60 to 80 percent of all that energy is consumed right in the base station due to the mostly horrible efficiency of the linear power amplifier in that base station. So, that says that of all the blocks in this network end-to-end, and in that power value chain, that far and away the largest consumer is the obvious place to start for focusing on improving energy efficiency in those. Now, what's also very fortunate for us, particularly as we look forward to 5G and more of a migration to small cells, and heterogenous networks or HetNets, is that as cells get smaller, their opportunity to employ intelligent power management techniques becomes better and more amenable. So, as a general rule of thumb, the smaller a device or system or whatever is, the lower its power budget will be, or its energy footprint, and, therefore, makes it more manageable. So like today, you know, we work on mostly on macro cell model, where there's a big tower that consumes many, many kilowatts and it services a greater area and number of users, and whatnot. But because of that, you can't just turn power on and off very quickly on those things, and typically it never fully goes off. But there's kind of known traffic patterns and they essentially try and adjust the energy that's being consumed in there, in that base station based on known traffic patterns so, if it's a time when it's used more, they turn up the power, and in the middle of the night, when it's not used as much, they try and turn it down. But, when you go to smaller cells and things that are talking about how the  power budget is on the order of 100 watts or less, as the cells get smaller, ideally someday even getting into things like picocells, and all that, where these small cells will run on ones of watts, that you can actually turn those on and off within say milliseconds, or tens of milliseconds. So, there's a lot of great features in the kind of the 5G technical spec, which is actually the 3GPP standard, that will enable more intelligent power management and the ability to shut things on and off quicker and ultimately at the end of the day, I always say there's nothing more efficient than something that's off. And then the second most efficient thing is something that's operating at its optimal point on its efficiency load curves. So that's why I think it’s, not only the biggest consumer, but also one of the best opportunities. And as I mentioned previously, you know, the ability to apply Energy Harvesting techniques to supplement energy budgets of devices at the edge should also be hugely enabling to reducing the burden on the base station, and therefore mitigates a lot of that very high power cost factor energy requirement. And the only other thing I'd like to mention of that is for opportunities for savings within the base station is, as I mentioned before, the linear power amplifier, the LPA, is typically the worst part, the lowest efficiency part of that base station. So, the reason base stations in general consume 60 - 80 percent of the global energy footprint, is because the-- what is typically a linear power amplifier that's used -- has efficiencies at best, you're lucky if you're in the double digits. So, you know, we're talking about 10, 12 percent efficient. So, that means for your wireless transition, you know, 80 to 90 percent typically-- well, actually more like 90, 90-plus percent, of that energy is just being lost to heat in the base station, and so therefore, just-- even just a maniacal focus on improving the efficiency of power amplifiers in the base station is the overall single best place to focus on for improvement, has the most room for improvement, and also would have the most impact on the global footprint. And to that end, I know there are some folks who are really focused on that, including some that are actually involved with our initiative and even our Energy Efficiency Working Group to really move the needle in that regard.

Brian Walker:  So, does power efficiency return power directly to the user?

Brian Zahnstecher:  Not really. It's more in the way of mitigating, generating a lot of stuff. A lot of extra energy. So, in other words we talked about how if I have to source my energy requirement at the edge from the power plant or whatever, I have to pay that very high power cost factor that's ten to the fifth, ten to the sixth. But, if I simply can supplement some of that micropower at the edge, then at the device itself, I can reduce the burden on the base station from the amount of power it needs to transmit, and so therefore, my power cost factor at the edge is really almost nil, right? It's a factor of one, because you're self-generating it right there, and you're not paying for it upstream. So, by being able to do that, you're relieving that base station and everything upstream in the power value chain up to the grid and the power plant, and that's really the true value. Because for one, if what I'm hypothesizing about the 5G Energy Gap is true, then this isn't just about a convenience to the users, saving some load, it could be the difference between the utility grid being able to support the 5G network, or being perhaps be stabilized or at worst, going down. And so, there's a very indirect impact to everyone, whether they're using the network or not. I mean, imagine a scenario where because you deployed a whole bunch of little tiny sensor networks, and endpoints locally, that it causes the local utility grid to be destabilized or go down, and people lose power to their homes. That's not just talking about quality of service for your 5G connection, now it's, "Oh, I don't have power to the home, and now that's a really serious issue. So, even people who don't even know what 5G is, all they will know is they're getting impacted in a serious way because their power goes down. And so even if they're not aware of it, proactively addressing that issue and mitigating it with something like Energy Harvesting, does provide them that value and sparing them the hardship of losing power and all that. From a direct kind of power bill savings perspective, it's more like, again, supplementing that budget at the edge, it's just what you prevent from having to be generated, that the real benefit ripples through to the whole network. And hopefully people would also argue that there's a benefit of saving and mitigating major generation also ties directly into mitigating carbon generation, as well as all the other ancillary environmental, as well as business and technical, factors that go into that.

Brian Walker:  Clearly this is a complex subject. Where can people go to learn more?

Brian Zahnstecher:  Well, there's several resources. One great thing just around this 5G Energy Gap thing, in particular, there was an article that I wrote that I put in the IEEE Power Electronics Quarterly Magazine in the December issue, which is out there, and free and open to the public, which you can just go to Google and put in my last name and 5G Energy Gap, or something like that, it'll be easy to find. And I think more importantly within the IEEE Future Networks Initiative, there is a roadmapping effort and we've recently kicked off an Energy Efficiency Working Group, which is openly tasked with comprehensively putting together all this information, identifying the problems, the solutions, and all that, with a kind of three/five/ten-year roadmap outlook. So, that recently, that had just kicked off! As a matter of fact, this Friday will the first meeting of that Working Group. But initially, we have a whitepaper that we're working on putting together, that will be distributed by the initiative in the near future. So, if you also go to the International Network Generation Roadmap under the Future Networks Initiative, there's plenty of resources there, including the information about this Working Group, the first edition Roadmap materials, which are already out there, as well as the information on-- as these things come out like our whitepaper, and eventually, a full roadmap chapter in the second edition of the Roadmap, which I don't have a specific target date, but I would assume is roughly within a year's timeframe or so. And in addition to that, you know, I'm sure there's plenty of other things -- I give a lot of talks in this space, as well as do a lot of the colleagues that I work with in the Working Groups and initiatives that I've mentioned. And other than that, feel free to reach out to me! My content info is always out there, and you do a search in Google for my last name, especially anything to do with power or energy or whatever, and I'm pretty sure I'm the only Zahnstecher that will come up in that regard. Certainly, the only Brian Zahnstecher! And I'm always happy to field information about this, enable people with information, you know, evangelize and preach the awareness and points about this and energy harvesting, and I'm always happy to help get this important message out there, and internalize and understood by the industry.

Brian Walker:  Thank you for listening to this edition of the IEEE Future Networks Podcast with the Experts. Discover more about the IEEE Future Networks Initiative, and inquire about participating in this effort, by visiting our web Portal at FutureNetworks.IEEE.org.

 

 

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.

 

IEEE5G TelecomGenerations TwitterGraphicIEEE 5G Transmissions: Podcasts with the Experts
An IEEE Future Directions Digital Studios Production 

A podcast with Adam Drobot: Chair, IEEE IoT Activities Board, Recipient, IEEE Managerial Excellence Award, Chairman of the Board, OpenTechWorks, Inc.

Is it time to change how we think about telecommunications generations?

In this installment of the IEEE 5G Transmissions: Podcasts with the Experts, Adam Drobot explores the question of whether it’s time to change how we think about telecommunications generations – do we still need discrete generations or are we in an era of continuous change? Adam is a featured speaker at the IEEE 5G World Forum, taking place this July 9th through 11th in Santa Clara, CA. Find out more about the IEEE 5G World Forum at ieee-wf-5g.org

 

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IEEE5G SneakPeakSanjay TwitterGraphicIEEE 5G Transmissions: Podcasts with the Experts
An IEEE Future Directions Digital Studios Production 

A podcast with Sanjay Jha, General Chair, 2018 IEEE 5G World Forum, CEO, Roshmere, Inc. 

5G: Let's not wait for the killer app. A sneak peek at Sanjay Jha’s keynote at IEEE 5G World Forum

This installment of our podcast series provides a sneak peek at the keynote presentation Sanjay Jha will give at the IEEE 5G World Forum. Sanjay speaks to 5G and previous wireless communications generations and the idea of the killer app. Sanjay is the General Chair of  the IEEE 5G World Forum, taking place July 9th through 11th 2018 in Santa Clara, CA. Find out more about the IEEE 5G World Forum at ieee-wf-5g.org. Sanjay is also CEO of Roshmere, Inc.

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IEEE 5G Transmissions: Podcasts with the Experts 
An IEEE Future Directions Digital Studio Production 

In this installment of the IEEE 5G Transmissions Podcast: Podcasts with the experts, we talk about The Future of Mobile Beyond 5G with Mischa Dohler who is an IEEE Fellow and co-chair of the IEEE 5G Technology Roadmap Committee. He is a professor at King’s College London where he is directing the Centre for Telecommunications Research. Mischa’s vision is based on his experience helping design cellular systems over the past 18 years as part of university, industry and startup jobs.

 

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Recording Transcript: Part 1

The mobile industry has enjoyed tremendous growth over the past decades. It has evolved from a niche technology, embodied by an analogue first generation 1G voice system, to a fully-fledged Internet on the move, embodied by an end-to-end digital 4G system. With so many generations of mobile now deployed globally, the technology is starting to become commodity and is naturally experiencing market pressure underpinned by shrinking margins and higher deployment costs.

It is hence useful and timely to pose the question on the future of mobile, a future which goes beyond 5G. Notably, I would like you to understand which technology disruptions are required to enable mobile not only to survive but to thrive in an increasingly competitive technology and business landscape.

Now, let’s start with some trends: Decades of mobile development, deployment and usage allows us to draw fundamental trends: The first relates to an increase in orders of magnitude of the system key performance indicators, the KPIs. The second relates to the major constituent of capacity increase over past decades.

Let’s start with the first one: And the interesting fact here is that the KPIs of cellular have evolved in a rather consistent way from generation to generation. The most important ones are data rate, number of connected devices and delay/latency. Each of these have increased or decreased by one to two orders of magnitude from generation to generation. Notably, the downlink data rates evolved from 2G to 3G to 4G respectively from about 100kbps to 170Mbps to 1Gbps; the number of devices from hundreds of devices to 10,000 devices per km2; and latency has been reduced from almost 300ms to 100ms to 10ms.

5G and the evolutions thereafter are unlikely to follow a different trend. The International Telecommunication Union (the ITU) is shaping the requirements to be fulfilled by the future mobile generation in the IMT-2020 programme. While the recommendation is still being discussed, we can extrapolate some KPIs from the different use cases and applications. In particular, for the 5G downlink: data rates will be 10Gbps, the number of devices per km2 will be about 100,000, and the latency will be about 1ms for ultra-low latency use cases. I want you to understand that, for the first time, these numbers overstep some fundamental thresholds which make 5G very interesting for stakeholders which traditionally were not associated with cellular technologies.

Indeed, the extremely high number of devices (and optimised power consumption) allows 5G to enable the emerging Internet of Things (IoT) which requires billions of end-points to be connected. Given the global coverage, along with mobility and roaming support, 5G is hence consolidating as a serious candidate to enable the IoT.

Furthermore, the very low latencies and ultra-high reliability, enables critical applications to be serviced. Given the ability to offer service level agreements (that is SLAs), 5G is hence also consolidating as a serious candidate to enable Industry 4.0 applications.

Therefore, the evolution of the KPIs will allow 5G not only to address consumer needs but also a wide range of industry use cases.

Concerning the 2nd fundamental trend, let’s examine the breakdown on the increase of wireless capacity over the past three decades. We shall make use of Martin Cooper’s law which says that wireless capacity doubled every 30 months over the past 100 years with overall million-fold increase in capacity since 1957, with the breakdown of these gains being: 5 times due to the physical layer enhancements, 25 times due to spectrum, and a whooping 1600 times due to the massive deployment of reduced cells. Overall, it indicates that smaller cell sizes are by far the biggest contributor, followed by the availability of spectrum; all the other factors however remain negligibly small. In particular, the physical layer has only contributed roughly 0.3% to the increase in capacity when compared to that of the smaller cell sizes, whereas spectrum accounts for 1.5%. It is due to these smaller cell sizes that cellular has become much more heterogeneous, and this trend is to continue, if not accelerate with 5G and beyond.

IEEE 5G Transmissions: Podcasts with the Experts 
An IEEE Future Directions Digital Studio Production 

This is the second installment of the IEEE 5G Transmissions: Podcasts with the Experts "The Future of Mobile Beyond 5G" with Mischa Dohler who is an IEEE Fellow and co-chair of the IEEE 5G Technology Roadmap Committee. He is a professor at King’s College London where he is directing the Centre for Telecommunications Research. Mischa’s vision is based on his experience helping design cellular systems over the past 18 years as part of university, industry and startup jobs.

 

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Part 1 of this recording is available here

Recording Transcript: Part 2

So, let’s spend some time to discuss technology disruptions which are needed to keep up with past trends and which ensure that telecoms does not become total commodity.

The first disruption is that we need to move from hard KPIs to the perception of KPIs. Indeed, with decreasing cell sizes and increasing traffic demand, it will become more and more difficult to offer satisfactory designs on rates, outage or delay. Therefore, I advocate for a fundamental change in the design approach where systems are not designed and regulated based on the measured KPI but on the perceived KPI.

Let us take the example of data rate. In a future 5G system, the majority of the capacity will be provided via some high capacity small cells using millimetre wave technologies. However, providing ubiquitous radio frequency coverage to satisfy such capacity increase is both technically challenging and economically prohibitive. One possible solution here is to use predictive analytics on different metrics, such as: consumer data usage behaviour, user movement behaviour or speed of movement. That would allow one to implement, for example, enhanced caching techniques which allow to provide service continuity in the case of a coverage hole, and buffer the to-be-watched Netflix or Youtube video until the next access point is reached. Several technical challenges need to be addressed here, with the most notable being that the application layer needs to communicate with lower link layers so as to execute the best strategy. Strategies based on the use of software defined networks (i.e. SDN) for dynamic QoS management can be further enhanced with predictive analytics to provide accurate on demand resource allocation.

Let’s move from user perception to the first segment in the tech chain, i.e. spectrum. Spectrum has been managed over past decades in two regimes: licensed (i.e. operators purchase spectrum rather expensively) or license-exempt (i.e. anybody can use it as long as simple rules are obeyed). Numerous studies have established that spectrum as a whole however remains poorly utilized.

What about managing spectrum in an unprecedented way through distributed ledgers, like used for block chains? This combines prior work on dynamic spectrum management, TV White Spaces, spectrum databases and blockchains. We would reach a new level of atomized spectrum ownership at a spatial and temporal granularity not seen before. You could be an operator, so could I, so could a small business. And all could be seamlessly managed in a trusted environment, ensuring maximum capacity and minimum interference. Wow!

Now, let’s talk about the next tech element, the radio access networking architecture where we have pioneered a novel atomized and decoupled architecture. Traditionally, due to homogeneous cell sizes, downlink and uplink were associated to the same base station. We have found out that under heterogeneous settings, decoupling both where uplink goes to a microcell base station and downlink comes from a macrocell not only gives great capacity gains but more importantly decreases outage by 1-2 orders of magnitude. The concept is called DUDe and we believe it to be a transformational design approach in cellular.

Let’s move on to the Core Network, where I have been advocating for years for a thinning of the core networking infrastructure.

Now, we have the CN today in 4G because we had it in 3G; we had in 3G because it was there in 2G. And the reason it was there in 2G is because at those pre-Internet times none of the operators believed that there will ever be a general Internet which would be able to carry the voice traffic. 30 years on, we still use the CN and thereby greatly limit the scalability of the wireless edge, which – because of above discussions – limits the rates to be delivered.

Capitalizing on this insight as well as recent trends to virtualize the enhanced packet core (vEPC) functionalities, the next step ought to be to push the entire cellular CN system to the edge: first, into the emerging Cloud-RANs; and later into the edge devices. This approach would allow to scale and – importantly – significantly decrease the end-to-end delay between operators.

And finally, I would like to introduce the concept of “Self-Designing Cellular Systems”.

With advances in artificial intelligence (AI), software defined radio/networks (SDR/SDN) and robotics, there is no reason why cellular system couldn’t evolve their own design and deployment. Whilst research on best possible technology solutions can still be conducted by humans, future cellular systems should be able to scout the publication/innovation databases such as IEEE Xplore, extract the most promising solutions, self-upgrade these (using SDR/SDN) and/or self-deploy them (using autonomous drones, for example). This would allow the standardization cycles to be shortened from years to days if not minutes.

Now, to finish off:

For these technology disruptions to take place, significant changes in the underlying innovation ecosystem are advisable. Without going into great details, I believe we first need a much stronger industry co-design approach; and second, we need to open up standards to accelerate the standardization cycles.

And finally, a thought for you to ponder about: Don’t you think we should innovate the regulators so they can properly regulate innovation in the 21st century?

Thank you for listening! And please do reach out via Twitter or email if you have feedback or ideas. Thank you!

If you want to know more about my thoughts, please, consult a paper published in 2017 as part of the EuCNC conference with a softcopy available from my website www.mischadohler.com.


IEEE 5G Transmissions: Podcasts with the Experts 

An IEEE Future Directions Digital Studio Production 

In this installment of the IEEE 5G Transmissions: Podcasts with the experts, we talk about the main transformative aspects of 5G with Dr. David Soldani. Dr. Soldani is an IEEE Senior Member and Associate Editor-in-Chief of IEEE Network Magazine. He is the head of 5G technology, end-to-end and global for Nokia Germany and he is an industry professor at the University of Technology in Sydney, Australia.

 

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Recording Transcript:

5G defined as the International Mobile Telecommunications for 2020 and beyond (5G) will expand and support diverse usage scenarios and applications with respect to current mobile network generations, purposed primarily for voice, mobile internet and video experience.

The agreed scenarios for 5G include:

1) “Enhanced mobile broadband (eMBB)” addressing human-centric use cases for access to multimedia content, services and data;

2) “Ultra-reliable-low-latency communications (URLLC)” with strict requirements, especially in terms of latency and reliability; and

3) “Massive machine type communications (mMTC)” for a very large number of connected devices and typically transmitting a relatively low volume of non-delay-sensitive information.   

5G technologies will efficiently enable new secure, dependable, ultra-reliable and delay-critical services to everyone and everything, such as cognitive objects and cyber-physical systems.

To realize this vision, 5G capabilities will include: A new flexible and efficient wireless interface, access schemes and other enabling wireless and network technologies, as well as a new plastic network architecture, supporting multi-tenant and new role models. With network slicing, different end-to-end logical networks with isolated properties are provided and operated independently. These enable operators to support different use cases, with devices able to connect to multiple slices simultaneously, and monetize network slice instances as a service.

Two architectures options are possible: 

  • The radio access will be based on New Radio (NR) in a Stand-Alone (SA) configuration of 5G systems – Devices, Next Generation (NG) Radio Access Network (NR), NG Core (or 5GC);
  • or Non-Standalone (NSA) NR in an Evolved Packet System (EPS), which is a dual connectivity 5G System deployment with an Evolved-UTRA, as the anchor Radio Access Technology (RAT), and NR as the secondary RAT, in a non-standalone configuration in Evolved Packet System.

5G is expected to increase spectrum efficiency and support contiguous, non-contiguous and much broader channel bandwidths than available to current mobile systems. 5G radio will be the most flexible way to benefit from all available spectrum options from 400 MHz to 90 GHz, including licensed, shared access and unlicensed, FDD and TDD modes, including supplementary uplink (SUL), narrowband and wideband allocations.

Millimeter wave spectrum above 20 GHz can provide bandwidth up to 1-2 GHz, which offers data rates up to 20 Gb/s and extreme mobile broadband capacity. The high bands are mostly suitable for local usage, such as mass events, indoor and outdoor hotspots and for fixed wireless access (FWA).

Spectrum at 3.5 GHz, 4.5 GHz and 4.9 GHz will be used for 5G coverage and capacity in urban areas reusing the existing sites. At those frequencies, the bandwidth can be up to 100 MHz per operator, and even up to 200 MHz with the re-farming of some of the existing bands. In fact, 5G coverage at 3.5 GHz, when using massive MIMO beamforming, can be similar to LTE 1800 or 2100.

Low bands, below 1 GHz, are needed for wide area rural coverage, for ultra-high reliability and for deep indoor penetration. Extensive coverage is important for new use cases such as IoT and critical communication. The low band could be 700 MHz, which is available in many countries, or 900 MHz, today mostly used by 2G and 3G systems. In the USA, another option for 5G low band is 600 MHz.

The new 5G radio is for all spectrum options. To this end, 5G supports a flexible numerology, which consists of different sub carrier spacing, nominal cyclic prefix, and transmission time intervals, or scheduling intervals, depending on bandwidth and latency requirements. Sub-carrier spacings of 15 kHz to 120 kHz, and the corresponding cyclic prefix of 4.7 to 0.59 µs and scheduling interval of 1ms to 0.125ms, are defined for different carrier components, which may vary from 5 MHz to 100MHz, below 6GHz, and from 50 to 400MHz, above 6GHz.

For optimal radio performance, the higher the carrier frequency, the higher the allowed carrier component and subcarrier spacing, the lower the corresponding cyclic prefix and scheduling period. If we consider a typical 5G deployment at the 3.5 GHz band, the bandwidth could be 40-100 MHz, the subcarrier spacing 30-60 kHz and minimum scheduling period 0.125 ms. The corresponding numbers in LTE are 20 MHz bandwidth, 15 kHz subcarrier spacing and 1 ms scheduling period. In the narrowband cases where low latency is required, the so called ‘mini-slot’ can be used in 5G, where the transmission time may be reduced to 2, 4 or 7 OFDM symbols. It is also possible to combine multiple slots together.

Massive MIMO (mMIMO) will be an integral part of 5G from day one, including common and control channels with beam forming and tracking. With mMIMO the number of transmitting antenna elements is much higher than the number of MIMO streams (or layers). In practice, mMIMO means that the number of controllable antenna elements is more than eight.

5G radio will support 8 Layers Single User (SU)-MIMO or 16 Layers Multi User (MU)-MIMO in the downlink, and 4 Layers SU-MIMO in the uplink, with the possibility of dynamic switching in both directions. Multiuser MIMO means that parallel MIMO data streams (or layers) are transmitted to different users at the same time-frequency resources. A typical example of 16 Layers configuration is 2layer/UE×8UE MU-MIMO.

Beamforming offers the advantages that the same resources can be reused for multiple users in a cell: It allows Space Division Multiple Access (SDMA), maximizing the number of supported users within that sector: It minimizes interference and increases cell capacity. As a result, Massive MIMO and active antenna technologies (AAT) give higher spectral efficiency and provide much more capacity on existing base station sites. The technology can also enhance link performance and increase the coverage area.

5G radio design is fully optimized for massive MIMO using three basic techniques for forming and steering beams:

  • Digital beamforming, where each antenna element has a transceiver unit with the adaptive Tx/Rx weights in the baseband, enabling frequency selective beamforming. Digital beamforming boosts capacity and flexibility and it is mostly suited to bands below 6 GHz.
  • Analog Beamforming implements only one transceiver unit and one RF beam per polarization. Adaptive Tx/Rx weighting on the RF is used to form a beam. This is best suited for coverage at higher mmWave bands and offers low cost and complexity.
  • Hybrid beamforming is a combination of analog and digital beamforming. When some beamforming is in the analog domain, the number of transceivers is typically much lower than the number of physical antennas, which can simplify implementation, particularly at high frequency bands. This technique is suited to bands above 6 GHz.

The radio interface protocol stack is also optimized for cloud and distributed computing, with a flexible front-haul split, between high-layer protocols (PDCP and RLC), and low-layer (layer 1) peer entities, using the Enhanced Common Public Radio Interface (eCPRI). This will allow to relax the transmission capacity and the utilization of Ethernet. For example, assuming 100 MHz band, a 3-sector site with 64TX/RX mMIMO and 16 layers, the interface between the radio unit (RU) and edge cloud (radio access unit, RAU) would require: 1Tb/s with no split, using a CPRI interface; 150 Gb/s with low layer split and enhanced CPRI (eCPRI) interface; and 1-10 Gb/s with high layer split. The latency with low layer split is expected to be below 0.1 ms, as with CPRI; and 5 ms with high layer split, which is still satisfactory for ultra-reliable low latency communications (URLLC).

Compared to LTE1800 with 2x2 MIMO, as baseline, 5G gives 10-20x additional capacity, being 2-4 times more spectrally efficient. For example, LTE with 20MHz band achieves 40 Mb/s cell throughput, which corresponds to a spectrum efficiency of 2b/s/Hz; 5G with mMIMO beamforming at 3.5 GHz and 100MHz band reaches 400-800 Mb/s cell throughput, corresponding to a spectrum efficiency of 4-8 b/s/Hz.

The radio interface latency with 5G is also dramatically reduced compared to previous releases. The target is 1 ms in idle, and connected mode with and without uplink resources allocated. 5G solutions to low latency are: Shorter transmission time interval, connected inactive state and contention based uplink.

5G is not only about radio!

Additional benefits will come with the introduction of the 5G core, which supports many new enabling network technologies. For example, the 5GC is characterized by a layered and service oriented architecture, with control plane and user plane split and shared data layer, for subscription, state and policy information. It also supports: User plane session continuity, while the terminal moves across different access points; interworking with untrusted access; a comprehensive policy framework for access traffic steering, switching and splitting; and wireless-wireline convergence.

Other fundamental 5G enabling technologies, end-to-end, are: Flow-based QoS, with a much higher level of granularity than LTE, which is currently limited to the bearer service concept; multi-connectivity, where the 5G device can be connected simultaneously to 5G, LTE and WiFi, offering a higher user data rate and a more reliable connection; terminal assisted Network Slicing, and end-to-end (E2E) network management and orchestration, with in-built support for cloud implementation and edge computing.

5G Network Slicing comes along with new information and role models, and slice management functions, responsible for the management and orchestration of network slice instances (NSI). An NSI consists of one or more network slice subnet instances (NSSI) of physical network functions (PNF) and/or virtualized network functions (VNF).

Within this framework, three main role models are defined, namely:

  • The Communication Service Customer (consumer, enterprise, vertical, CSP, etc.), who may use communication services from a Communication Service Provider (CSP);
  • the Communication Service Provider builds its offering on top of network services, from the Network Operator, and virtual infrastructure services, from the Virtual Infrastructure and Data Center Service Providers; and
  • the physical and virtual network functions composing the network slice instances, end to end, may be provided by Network Equipment Vendors (including VNF), Network Function Virtualization Infrastructure Suppliers, and Hardware Suppliers.

It is of course intended that an organization may play one or several roles.

The new CSP offering, enabled by 5G Slicing, is Network Slice as a Service (NSaaS). Like cloud computing SaaS, IaaS and PaaS models, the Communication Service Customer, i.e. the tenant, may compose, order and pay one or more network slice instances based on its utilization; service level agreements (SLA), e.g. in terms of latency, throughput, and reliability; and value-added services (VAS).

In practice, 5G will support three basic business models for network slicing, depending on the tenant’s degree of slice control, which may go from monitoring only the KPIs related to the signed SLAs, changing the configuration of the deployed slice instances, to chaining own physical/virtual network functions. 

The partitioning model may be combined with the layering model to provide joint horizontal and vertical offerings. In slice partitioning, the orchestration of resources and capabilities, from an E2E service requirement perspective, must be horizontally federated (cooperation/collaboration), and vertically coordinated (hierarchy) through policies and standardized Interfaces/APIs.

The E2E 5G systems will consist of six domains, from terminal (UE) to the data network (DN) and service applications:

  1. Terminals: supporting Network Slice Selection Assistance Information, to request specific slice instances, based on the communication services in use.
  2. Access: eLTE/NR Radio Units with Ethernet front-haul (eCPRI) or ethernet mid-haul for low latency and latency insensitive services, respectively.
  3. Aggregation: Radio Clouds with their own Software Defined Network Controllers (SDN-C) and Virtual Infrastructure Managers (VIM).
  4. Mobile core: Core Cloud with own SDN-C and VIM interconnected to the Radio Clouds by IP routers and WAN SDN-C.
  5. Network Slice Management and Orchestration: An E2E Service Orchestrator for the embedding of Network Service Descriptors (Network Connectivity topology and VNF forwarding graphs), on top of a Self-Organizing Network (SON) and VNF Manager functions.
  6. Data Layer and Application Enablement: g. IoT and Customer Experience Management (CEM) platforms for running applications on top of the different network slices for public safety, digital health, mobility, industry automation, smart cities, etc.

Artificial Intelligence, in terms of descriptive, predictive and prescriptive analytics, will find application in three main areas:

  • SON: Key capabilities / algorithms / architecture attributes within the different domains (RAN, Core, Transport etc.) to enable the right flexibility and tradeoffs for operators to efficiently exploit slicing;
  • Data and application layers, i.e. big data analytics (structured data analytics, text analytics, web analytics, multimedia analytics, network analytics, mobile analytics), and
  • Data-layer platforms for IoT and CEM.

To realize the 5G vision I have just presented: spectrum must be made available first, global standards, next, and regulations must follow. It is also necessary a massive investment from industry, especially from Connectivity Service Providers (Operators).

Thank you!

 


IEEE 5G Transmissions: Podcasts with the Experts
An IEEE Future Directions Digital Studio Production 

In this edition of IEEE 5G Transmissions: Podcasts with the Experts, subject matter experts from the IEEE 5G Initiative, the IEEE Green ICT Initiative, the IEEE SDN Initiative, and the IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems offer their insights on the question: What is your boldest vision of what 5G can bring us? 

 

 

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The experts in order of appearance:  

Rob Fish 

Member, IEEE 5G Steering Committee
Vice President of Industry and Standards Activities, IEEE Communications Society (ComSoc)

 

 

 

Eileen Healy

Co-chair IEEE SDN Initiative
Member, IEEE 5G Steering Committee

 

 

 

 Charles Despins, Ing., Ph.D. 

Co-chair IEEE Green ICT Initiative

 

 

 

John C. Havens

Executive Director, The IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems

 

IEEE5G 234x140 Podcast1

IEEE 5G Transmissions: Podcasts with the Experts
An IEEE Future Directions Digital Studio Production 

In this inaugural installment of IEEE 5G Transmissions: Podcasts with the Experts, several subject matter experts and members of the IEEE 5G Initiative Steering Committee offer their insights on the question: What challenges do you foresee that could affect deployment of 5G? 

 

 

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The experts in order of appearance:  

chih lin croppedChih-Lin I, Chief Scientist of Wireless Technologies in charge of advanced wireless communication R&D efforts, China Mobile Research Institute, and Founder, Green Communication Research Center of China Mobile, Co-Chair IEEE 5G Initiative Publications Working Group, Member IEEE 5G Steering Initiative Committee

 

 

 

Adam Drobot Adam Drobot, Chairman of the Board, OpenTechWorks, Inc., Member IEEE 5G Initiative Steering Committee

 

 

 

condry croppedMichael Condry, Former CTO, Intel Global Ecosystem Development Division (retired) and President, IEEE Technology and Engineering Management Society, Co-Chair IEEE 5G Initiative Industry Engagement Working Group, Member IEEE 5G Initiative Steering Committee

 

 

 

ashtosh cropped podcastAshutosh Dutta, Lead Member of Technical Staff at AT&T, Co-Chair IEEE 5G Initiative

 

 

 

james croppedJames Irvine, Reader in Department of Electronic and Electrical Engineering, University of Strathcylde in Glasgow, Scotland where he leads the Mobile Communications Group. Chair IEEE Intiative 5G Web Portal / Content Development Working Group, Co-Chair of IEEE 5G Initiative Community Development Working Group, Member IEEE 5G Initiative Steering Committee 

 

 

alex croppedAlex Wyglinski, Associate Professor, Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, and Director and Founder Wireless Innovation Laboratory, Co-Chair IEEE 5G Initiative Community Development Working Group

 

 

 

IEEE 5G is polling audiences on Twitter and Collabratec to weigh in on the same question. Please visit and vote for your choice. Check back soon for the results! 

What challenges do you foresee that could affect deployment of 5G? 

a. Spectrum
b. Scalability
c. Interoperability between equipment vendors 
d. Other 

 

 

 

IEEE 5G Podcasts with the Experts Series

 We bring the experts to you! Through our podcast series, the IEEE Future Networks Initiative interviews top subject matter experts and visionaries in 5G and future generations of networking. Covering many topics including the current and coming convergence of rapidly developing new and existing individual technologies to support next generation networking, IEEE Future Networks Podcasts with the Experts provides you with access to the industry's top subject matter experts.

 

Get it on iTunes Listen on Google Podcasts   Listen on Spotify

 

 

 

 

Topic Description Date

The 5G Energy Gap - A Fatal Flaw for 5G Deployment? 

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As massive 5G networks are being deployed in full speed in 2020, there is a potentially fatal flaw lurking in the supporting infrastructure, the utility distribution network. The 5G Energy Gap describes the uncertain ability of the utility grid to meet load energy requirements of potentially billions of devices while maintaining grid reliability. The Internet of Things, Industrial Internet of Things, edge computing and other technologies and network trends increase the issue exponentially.

Energy Harvesting (EH) solutions can supplement or even mitigate the multitude of tiny power requirements of systems where it matters most, at the edge. Scavenging every form of physical, ambient energy from the surrounding environment, EH spares the utility grid and power plants, and is a critical factor in addressing the 5G Energy Gap,

 May 2020 

The International Network Generations Roadmap - Executive Overview 

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The International Network Generations Roadmap (INGR) is stimulating 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. 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. The INGR, created by experts across industry, government, and academia, helps guide operators, regulators, manufacturers, researchers, and others involved in developing new communication technology ecosystems by laying out a technology roadmap with 3-year, 5-year, and 10-year horizons. 

 April 2020 

Do We Still Need the FCC? Darpa's Spectrum Collaboration Challenge

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In this episode, Paul Tilghman, DARPA program manager, speaks to the three-year-long Spectrum Collaboration Challenge that attempts to answer the question, Do we still need the FCC? DARPA, the United States Defense Advanced Research Projects Agency, gamified a system to handle Dynamic Spectrum Sharing through the creation of SDN radios using the power of artificial intelligence and collaborative autonomy to navigate, share and optimize wireless spectrum in a testbed called Colosseum, and invited the world to compete. The live championship event takes place on October 23 at Mobile World Congress LA, and will be live-streamed.

 

October 2019

5G Connectivity Beyond the City - Agricultural Use Cases Through 5G RuralFirst 

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In this episode, James Irvine talks with Karina Maksimiuk and Greig Paul about their work with 5G RuralFirst, a UK government testbed and trial project, which describes itself as a ‘call to action’ to be sure the benefits of 5G go beyond the city.

September 2019 

5G for large-scale wireless communications between autonomous vehicles

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In this episode of IEEE 5G Transmissions: Podcasts with the Experts, Alex Wyglinksi speaks to how 5G can be used with cognitive radio and vehicular dynamic spectrum access to support large-scale wireless communications between autonomous vehicles. Alex is co-chair, IEEE 5G Community Development Working Group, president of the IEEE Vehicular Technology Society and a full Professor of Electrical and Computer Engineering at Worcester Polytechnic Institute  July 2018

5G: Let's not wait for the killer app. A sneak peek at Sanjay Jha's keynote at IEEE 5G World Forum 

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This installment of our podcast series provides a sneak peek at the keynote presentation Sanjay Jha will give at the IEEE 5G World Forum. Sanjay speaks to 5G and previous wireless communications generations and the idea of the killer app. Sanjay is the General Chair of  the IEEE 5G World Forum, taking place July 9th through 11th 2018 in Santa Clara, CA. Find out more about the IEEE 5G World Forum at ieee-wf-5g.org. Sanjay is also CEO of Roshmere, Inc. June 2018 

Is it time to change how we think about telecommunications generations? 

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In this installment of the IEEE 5G Transmissions: Podcasts with the Experts, Adam Drobot explores the question of whether it’s time to change how we think about telecommunications generations – do we still need discrete generations or are we in an era of continuous change? Adam is a featured speaker at the IEEE 5G World Forum, taking place  July 9th through 11th 2018 in Santa Clara, CA. Find out more about the IEEE 5G World Forum at ieee-wf-5g.org.

May 2018

The Main Transformative Aspects of 5G


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In this installment of the IEEE 5G Transmissions Podcast: Podcasts with the experts, we talk about the main transformative aspects of 5G with Dr. David Soldani. Dr. Soldani is an IEEE Senior Member and Associate Editor-in-Chief of IEEE Network Magazine. He is the head of 5G technology, end-to-end and global for Nokia Germany and he is an industry professor at the University of Technology in Sydney, Australia.

November 2017

The Future of Mobile Beyond 5G Part 2

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This is the second installment of the IEEE 5G Transmissions: Podcasts with the Experts "The Future of Mobile Beyond 5G" with Mischa Dohler who is an IEEE Fellow and co-chair of the IEEE 5G Technology Roadmap Committee. He is a professor at King’s College London where he is directing the Centre for Telecommunications Research. Mischa’s vision is based on his experience helping design cellular systems over the past 18 years as part of university, industry and startup jobs.

September 2017

The Future of Mobile Beyond 5G Part 1

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In this installment of the IEEE 5G Transmissions: Podcasts with the Experts, we talk about The Future of Mobile Beyond 5G with Mischa Dohler who is an IEEE Fellow and co-chair of the IEEE 5G Technology Roadmap Committee. He is a professor at King’s College London where he is directing the Centre for Telecommunications Research. Mischa’s vision is based on his experience helping design cellular systems over the past 18 years as part of university, industry and startup jobs.

August 2017

What is your boldest vision of what 5G can bring to us?

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In this edition of IEEE 5G Transmissions: Podcasts with the Experts, subject matter experts from the IEEE 5G Initiative, the IEEE Green ICT Initiative, the IEEE SDN Initiative, and the IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems offer their insights on the question: What is your boldest vision of what 5G can bring us? 

March 2017

What challenges do you foresee that could affect deployment of 5G?

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In this inaugural installment of IEEE 5G Transmissions: Podcasts with the Experts, several subject matter experts and members of the IEEE 5G Initiative Steering Committee offer insights regarding deployment challenges of 5G.  Jan 2017