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

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

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

Abstract 

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

1. Introduction 

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

2. Delay-Aware SDT Design 

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

DelayModelingFig1

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

 

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

3. E2E Packet Delay Modeling 

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

DelayModelingFig2

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

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

DelayModelingFig3

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

 

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

DelayModelingEqn1v2

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

DelayModelingEqn2and3

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

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

 DelayModelingFig4

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

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

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

DelayModelingFig5

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

 

4. Simulation Results 

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

DelayModelingFig6

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

5. Conclusion 

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

DelayModelingFig7

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

Acknowledgement 

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

References 

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

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

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

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

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

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

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

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

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

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

 

YeDelayModeling

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

 

 

zhuangDelayModeling

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

 

LiDelayModeling

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

 

RaoDelayModeling

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

 

 

Editors: Chih-Lin I and Haijun Zhang

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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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 

 

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? 

 

 

Click here to listen. 
Click here to download. 

 

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? 

 

 

Click here to listen. 
Click here to download. 

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 Play Music

 

 

 

 

TopicDescriptionDate

 5G for large-scale wireless communications between autonomous vehicles

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Click here to download. 

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 

Click here to listen. 
Click here to download.

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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Click here to download. 

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


Click here to listen. 
Click here to download.
Click here to read transcript. 

 

 

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

Click here to listen. 
Click here to download. 
Click here to read transcript. 

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

Click here to listen. 
Click here to download. 
Click here to read transcript. 

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?

Click here to listen. 
Click here to download

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?

Click here to listen. 
Click here to download. 

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

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

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

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

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

* required field

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


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

On this page: 

 

About the IEEE Beyond 5G Roadmap

 Based on horizon scanning, interviews and expert knowledge, the mission of the 5G Roadmap working group is to identify short (~3 years), mid-term (~5 years) and long-term (~10 years) research, innovation and technology trends in the communications ecosystem. This will enable the development of a concrete innovation and engagement roadmap guiding the IEEE community towards maximum impact contributions across its societies, and in conjunction with its demand-side as well as the wider industry and standards ecosystem. The outcome shall be a live document with a clear set of (accountable) recommendations; the document shall be updated annually and be developed in conjunction with the other working groups.

Working Group Teams

Working Group  Chairs  Email to contact to participate 
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Deployment This email address is being protected from spambots. You need JavaScript enabled to view it. This email address is being protected from spambots. You need JavaScript enabled to view it. 
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Working Group Team Meetings Schedule (all times in Eastern time) 

 Roadmap team members should check calendars for meeting information.

Tuesday Wednesday Thursday Friday

 9:00 am Security 
12:00 pm Applications & Services 
1:00 pm Standardization Building Blocks

 9:30 am mmWave & Signal Processing 
11:00 am Edge Automation Platform 

 11:30 Leadership/Co-Chair 

11:00 am Massive MIMO
11:00 am Hardware
12:00 pm Testbed 
2:00 pm Satellit

If you wish to join a roadmap working group and be included on these calls, contact the team chair.

Reports

News

 

Upcoming Events

 

Past Events

  

Call for Papers 

IEEE Future Networks Podcasts with the experts  

IEEE Future Directions Talks Future Networks: Q&A's with the experts

IEEE Publications on 5G and Future Networks

White Papers 

 

IEEE XploreSearch IEEE Xplore for articles on 5G

 


Call for Papers

Upcoming calls for papers in IEEE Future Networks Sponsored Special Issues include: 

IEEE 5G World Forum 2019 Call for Papers, Vertical/Topical Areas, and more 
Submission Deadline: 15 April 2019 

Call for Papers on 5G and Future Networks 

IEEE International Conference on Sensing, Communication, and Networking
Submission Deadline: 1 February 2019 

IEEE International Black Sea Conference on Communications and Networking 
Submission Deadline: 4 February 2019 

IEEE Access: Roadmap to 5G: Rising to the Challenge
Submission Deadline: 15 February 2019 

Fog Radio Access Networks (F-RANs) for 5G: Recent Advances and Future Trends
Submission Deadline: 15 February 2019

Sustainable Infrastructures, Protocols, and Research Challenges for Fog Computing
Submission Deadline: 28 February 2019

IEEE Network: Recent Advances in Security and Privacy for Future Intelligent Networks  
Submission Deadline: 20 March 2019

2019 IEEE 90th Vehicular Technology Conference: VTC2019-Fall 
Submission Deadline: 25 March 2019 

2nd IEEE Connected and Automated Vehicles Symposium 
Submission Deadline: 15 April 2019 

IEEE Vehicle Power and Propulsion Conference 
Submission Deadline: 21 April 2019 

Advances in Statistical Channel Modeling for Future Wireless Communications Networks
Submission Deadline: 30 April 2019

Advances in Signal Processing for Non-Orthogonal Multiple Access
Submission Deadline: 31 May 2019

Artificial Intelligence for Physical-Layer Wireless Communications
Submission Deadline: 31 May 2019

New Waveform Design and Air-Interface for Future Heterogeneous Network towards 5G
Submission Deadline: 30 June 2019

Information Centric Wireless Networking with Edge Computing for 5G and IoT
Submission Deadline: 30 June 2019

Millimeter-wave and Terahertz Propagation, Channel Modeling and Applications
Submission Deadline: 30 June 2019

Mobile Edge Computing and Mobile Cloud Computing: Addressing Heterogeneity and Energy Issues of Compute and Network Resources
Submission Deadline: 30 July 2019 

IEEE Journal on Selected Areas in Communications: Multiple Antenna Technologies for Beyond 5G 
Submission Deadline: 1 September 2019


Publications

VTM SI portalPicDec2018

 IEEE Vehicular Technology Magazine

IEEE 5G Initiative Special Issue on Zeroing in on Nonorthogogonal Multiple Access

December 2018 

 

 

 

VTM SI portalPicJune2018IEEE Vehicular Technology Magazine

IEEE 5G Initiative Special Issue on New Considerations for 5G Wireless Systems

June 2018 

 

 

 

IEEE Vehicular Technology Magazine

IEEE 5G Initiative Special Issue on 5G Technologies and Applications

December 2017 

 

 

 

 5G The New Wireless Frontier: Special Report on 5G by The Institute

The March 2017 issue of The Institute features many articles on 5G, including highlighting the IEEE 5G Initiative, upcoming standards for 5G, the many resources available at IEEE on the topic 5G, and others. 

 

 

IEEE 5G and Beyond Initiative - A Perspective, Mondo Digitale, February 2018 
An Opportunity to Contribute - IEEE's 5G and Beyond Initiative, Dr. Ashutosh Dutta, Co-founder, IEEE 5G Initiative 

The Institute
IEEE Releases Details About Its 5G and Beyond Roadmap, January 2018 
How 5G Could Prevent Internet Disruptions During a Natural Disaster, January 2018 
How 5G Could Bring Internet Access to Remote Areas
, May 2017 
The Five Foundational Technologies that Will Make Up 5G Networks, March 2017
5G: The Future of Communications Networks, March 2017
The IEEE 5G Revolution, March 2017
Timeline: The Evolution of 5G, March 2017  

IEEE Spectrum

raceto5gThe Race to 5G: The Latest 5G News and Analysis

5G is the next generation of wireless technology scheduled to arrive in 2020. Once here, 5G should help wireless networks provide more bandwidth, higher data speeds, and lower latency to many more electronic devices. It’s also one of the most hyped topics in technology—with enthusiasts promising it will be the gateway to self-driving cars, virtual reality, and the Internet of Things. Here, IEEE Spectrum follows 5G news from around the world as telecommunications companies develop standards, test new technologies, and prepare to roll 5G out to customers.

Test and Measurement is a Major Hurdle for 5G, November 2018 
Massive MIMO Will Create More Wireless Channels, But Also More Vulnerabilities, November 2018 
The 5G Dilemma: More Base Stations, More Antennas - Less Energy?, October 2018 
Three Trends Driving the 5G Test Paradigm
New "Network 2030" Group Asks: What Comes After 5G?, August 2018 
5G Poised for Commercial Rollout by 2020, May 2018
Invisible Connections will Unveil our 5G Future
Two U.S. Cities Win Support for 5G Wireless Test Beds, April 2018 
Automotive Applications of Device-to-Device Communication in 5G Networks
Mobile World Congress 2018: 5G's Killer App May Not Be an App at All, March 2018 
A Beam-Steering Antenna for 5G Mobile Phones, January 2018 
What Does Every Engineer Need to Know about 5G?
, January 2018 
5G New Radio and What Comes Next, January 2018 
CES 2018: 5G News and Nuggets
, January 2018
5G's Olympic Debut, January 2018 
Invisible Connections will Unveil our 5G Future, December 2017
5G Bytes: Small Cells Explained, August 2017
5G Bytes: Millimeter Waves Explained, May 2017
Facebook Aims to Remake Telecom with Millimeter Waves and Tether-tennas, April 2017
The 5G Frontier: Millimeter Wireless, February 2017
New Terahertz Transmitter Shines with Ultrafast Data Speeds, February 2017
Everything You Need to Know about 5G, January 2017 
Here comes 5G - whatever that is, January 2017

IEEE ComSoc Technology News
What Will 6G Be? June 2018 
The Challenges of 5G in a Cloud Based Network, April 2018 
A View of the Way Forward in 5G from Academia, December 2017 
Wireless Winners and Losers: Who's Making Money in the 4G to 5G Gap? October 2017 

IEEE Software Defined Networks (SDN) Initiative Newsletter 
5G Network Slicing and Security, January 2018
5G:  Platform and Not Protocol, January 2018 
SDN and NFV Evolution Towards 5G
, September 2017 
Addressing Key Challenges in 5G Networks with "Intent-Driven Networking", January 2017
Challenges of Network Slicing, January 2017 
Orchestration and Control Solutions in 5G: Challenges and Opportunities from a Transport Perspective, September 2016
Open Baton: A Framework for Virtual Network Function Management and Orchestration for Emerging Software-Based 5G Networks, July 2016
5G Era Networks: The Case for Open Mobile Edge Cloud, March 2016 
Edge Definition and How it Fits with 5G Era Networks, March 2016
Mobile Edge Computing - An Important Ingredient of 5G Networks, March 2016
Towards Software Defined 5G Radio Access Networks, March 2016

anwerBook5G Networks: Fundamental Requirements, Enabling Technologies, and Operations Management, Editors: Anwer Al-Dulaimi, Xianbin Wang, Chih-Lin I 

A reliable and focused treatment of the emergent technology of fifth generation (5G) networks

This book provides an understanding of the most recent developments in 5G, from both theoretical and industrial perspectives. It identifies and discusses technical challenges and recent results related to improving capacity and spectral efficiency on the radio interface side, and operations management on the core network side. It covers both existing network technologies and those currently in development in three major areas of 5G: spectrum extension, spatial spectrum utilization, and core network and network topology management. It explores new spectrum opportunities; the capability of radio access technology; and the operation of network infrastructure and heterogeneous QoE provisioning.


Articles on Future Networks can also be found in the following IEEE publications:


White Papers

IEEE 5G and Beyond Technology Roadmap White Paper, IEEE 5G Initiative 
A Look into the Future: The Applications Behind 5G, IEEE ComSoc white paper 
The Essential Resource Guide to 3GPP, An Overview and Update
, IEEE ComSoc white paper 
Real-time Prototyping of 5G Software Defined Networks: Part 2, IEEE ComSoc white paper 
Real-time Prototyping of 5G Software Defined Networks: Part 1, IEEE ComSoc white paper 
Towards 5G Software-Defined Ecosystems, IEEE SDN Initiative white paper