Role of SON for Joint Access and Backhaul Optimisation. Prof. Muhammad Imran (Mona Jaber and BT)

Transcription

1 Role of SON for Joint Access and Backhaul Optimisation Prof. Muhammad Imran (Mona Jaber and BT)

2 LATENCY THROUGHPUT BATTERY LIFE Diversity of 5G Challenges throughput Capacity x1000 coverage and mobility Smart meters >10min Realtime video ~Gbps Sensors ~1 decade spectrum and BW flexibility energy efficiency Latency <100µsec 5G Future IMT 4G IMT advanced 3G IMT 2000 reliability number of devices Tactile internet <1ms Fleet mngt ~kbps ~days 5G is challenging AND challenges are diverse 2

3 Areas for improvement Deployment Management Radio Resources Future Architectures Big Data Enabled SON Long term SON Short term SON Live-SON SON for split C/D SON for Joint Access/BH SON as an enabler for 5G 3

4 Ultra-dense 5G BH implications EPC 5G backhaul network coverage needs to match Macro-cell the spread of small cells. Small cell How to do that in a costeffective and feasible approach? Wireless or wired or both? Wireless bakhaul will increasingly become prevalent for dense deployment 4

5 Focus on Backhaul Network as well AP PST Internet Gateway (PDG, GGSN) Network Server (SGSN, HLR) BBU Media Server (IMS) Mobile Core Network Access Network 8-10% 85-90% 1-5% Energy Consumption (CO 2 -contribution) Significant Scope for Improvement Future mobile cellular networks (5G ) Future generations will need focus on improving backhaul as well 5

6 User Centric Backhaul PtP microwave Macro-cells Optical fibre RANaaS SGW Small cells mmwave xdsl In-band BHaaS Core Network Diverse users and QoS Relays RRH PtmP microwave HetNet Last mile BH/FH Backhaul RRH: Remote Radio Heads BBU: Baseband Unit HetNet: Heterogeneous Network PtP: Point ot point PtmP: point to multi-point BH: Backhaul FH: Fronthaul RANaaS: Radio access network as a service BHaaS: Backhaul as a Service SGW: Service gateway Diverse user demands need to be matched to diverse backhaul available 6 BBU

7 User-centric backhaul concept User centric choice optical fibre mmwave G.fast M1 A1 BBU 1 Ptmp microwave Aggregation/ router/splitter SC 1 A2 BBU 2 SC 2 M 2 A3 Need to influence user choice to select correct BH solution! 7 7

8 Access Point Selection Traditionally cells are ranked based on RSRP: R x =S x R M >R SC1 R SC2 >R SC3 With fixed CREO*=7dB on all small cells: R x =S x +CREO R SC1 R SC2 >R SC3 R M BUT SC2 can provide highest endto-end capacity due to ample BH SC3 has a wireless BH with relatively low resilience S M =-68dBm SSC1 =-70dBm S SC2 =-70dBm S SC3 =-75dBm Dynamically adjust multi-creos based on JOINT RAN/BH multi-attributes Core Network * CREO= Cell Range Extension Offset We may have multiple BH choices with different Access Points 8 8

9 Weighted CREO ω Tput =0 ω lat =2 ω EE =1 SC1 β Tput =0dB β lat =6dB β EE =2dB Small cell coverage based on received signal ω Tput =0 ω lat =0 ω EE =2 ω Tput =2 ω lat =2 ω EE =0 RX1=-70dB RX2=-70dB RX3=-75dB SC2 β Tput =0dB β lat =0dB β EE =6dB β Tput =6dB SC3 β lat =2dB β EE =2dB * CREO= Cell Range Extension Offset Need SON controlled dynamic CREO to match user preference with BH 9 9

10 User-centric tailored cell coverage Weighted CREO ω Tput =0 ω lat =2 ω EE =1 RX 1=-56dB RX 2=-64dB RX 3=--69dB SC1 β Tput =0dB β lat =6dB β EE =2dB Virtual cell ranges perceived by user 1 ω Tput =0 ω lat =0 ω EE =2 ω Tput =2 ω lat =2 ω EE =0 SC2 β Tput =0dB β lat =0dB β EE =6dB User 1 perspective * CREO= Cell Range Extension Offset SC3 β Tput =6dB β lat =2dB β EE =2dB 10 10

11 User-centric tailored cell coverage Weighted CREO ω Tput =0 ω lat =2 ω EE =1 SC1 β Tput =0dB β lat =6dB β EE =2dB Virtual cell ranges perceived by user 2 ω Tput =0 ω lat =0 ω EE =2 RX 1=-66dB RX 2=-58dB RX 3=-71dB β Tput =0dB β lat =0dB β EE =6dB ω Tput =2 ω lat =2 ω EE =0 SC2 User 2 perspective * CREO= Cell Range Extension Offset SC3 β Tput =6dB β lat =2dB β EE =2dB 11 11

12 User-centric tailored cell coverage Weighted CREO ω Tput =0 ω lat =2 ω EE =1 SC1 β Tput =0dB β lat =6dB β EE =2dB Virtual cell ranges perceived by user 3 ω Tput =0 ω lat =0 ω EE =2 β Tput =0dB β lat =0dB β EE =6dB ω Tput =2 ω lat =1 ω EE =0 RX 1=-64dB RX 2=-70dB RX 3=-61dB SC2 User 3 perspective * CREO= Cell Range Extension Offset SC3 β Tput =6dB β lat =2dB β EE =2dB 12 12

13 SON Mechanism KPI= Key Performance Indicator QoE= Quality of Experience CREO= Cell Range Expansion Offset BH link status Radio conditions RAN load RRH/Small Cell 1 Dynamic CREOs settings RRH/Small Cell N β 1 β 2 β A β 1 Dynamic Network Optimisation objectives Dynamic CREOs settings β 2 β A Measured dynamic NETWORK KPI Measured dynamic USERs QoE 13 13

14 Framework for Optimisation where, Find the set of CREO values (B) that will maximise cumulative effective throughput of network subject to: given that measured users QoEs (Q ) are as close as possible to target QoEs (Q) and that the backhaul capacity constraint (Kc) is not exceeded Backhaul overhead 14 14

15 SON Q-learning Initialise Q Choose action from Q Perform action Measure COST Update Q Actions are possible settings of CREOs Selected action is the one that gives the minimum cost Ultra high cost if backhaul capacity constraint is exceeded, else, cost is the QoE gap weighed by users preferences, else, cost is the difference between backhaul capacity and corresponding constraint 15 15

16 Case Study results Three attributes: Throughput, Latency, and Resilience A=3 M. Jaber, M. Imran, R. Tafazolli, and A. Tukmanov, A Multiple Attribute User-Centric Backhaul Provisioning Scheme using Distributed SON, submitted to IEEE Global Telecommunications Conference, GLOBECOM 2016, currently under revision 16 16

17 A=3: Throughput, Latency and Resilience Each small cell has dynamic and up-to-date link status information from its end-to-end fronthaul/backhaul connection that is randomly generated: Latency (min=10msec; max=100msec) Resilience (min=80%, max=99.999%) Three possible last-mile technologies are allocated randomly to each small cell with equal probability: G.fast (Achievable throughput depends on distance) PtmP microwave (600Mbps throughput shared among 7 cells) Fibre-based (1000Mbps) 600 randomly distributed users per sector with randomly generated with equal probabilities: binary weights to each of the 3 attributes (high or low) binary allocation of SINR target (high or low) Each small cell dynamically adjusts 3 CREO values: backhaul-related, latency-related, and resilience-related with the objective of: Maximising cumulative effective throughput (based on actual SINR if radio is limiting or backhaul throughput otherwise) Reducing the gap between users target and achievable throughput and latency Not exceeding backhaul capacity constraint 17 17

18 A=3: Throughput, Latency and Resilience 18 Network-centric performance Fixed CREO=12dB gives the best network centric results

19 A=3: Throughput, Latency and Resilience User-centric performance: Throughput-related QoE User-centric-BH rates best and allocates throughput-rich resources to users who value this attribute 19 19

20 A=3: Throughput, Latency and Resilience User-centric performance: Latencyrelated QoE User-centric-BH rates best, SINR-based scheme pushes users to macrocell with ideal BH for latency 20 20

21 A=3: Throughput, Latency and Resilience User-centric performance: Resilience-related QoE User-centric-BH rates best and allocates high-resilience resources to users who value this attribute 21 21

22 Cumulative throughput perceived by all served users (Mbps) CRE Usercentric-BH II. User-centric backhaul BH-aware- concept SINRbased Fixed- CREO=6dB Fixed- CREO=12dB Proportion of users in outage (%) Proportion of throughput shortage relative to users targets (for users that prioritise capacity) (%) Proportion of throughput shortage relative to users targets (for users that don t prioritise capacity) (%) Proportion of excess latency relative to users targets (for users that prioritise latency) (%) Proportion of excess latency relative to users targets (for users that don t prioritise latency) (%) Proportion of lack of resilience relative to users targets (for users that prioritise latency) (%) Proportion of lack of resilience relative to users targets (for users that don t prioritise latency) (%) User-centric-BH optimisation objectives are best or second best when inspected individually User-centric-BH is holistically best performing Hardest backhaul constraint is latency User-centric-BH saves operators 69% capacity upgrade, 57% reduction in latency, and 14% improvement in resilience

23 29 Conclusions Throughput, Latency and Resilience requirements are different for different services Different backhaul technologies provide these KPIs at different level Adapting the AP selection considering the backhaul capabilities will ensure a better match of requirement and the offering User centric backhauling will result in better QoE in end users.

24 Thank UofGlasgow Search: University of Glasgow