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Spatially-temporal traffic dynamics and URLLC service

Spatially-temporal traffic dynamics and URLLC service. Outline. 2. 5G services Applications for 5G NR Part 1: Servicing spatially-temporal traffic New AR/VR service characteristics Mesh-based D2D incentivized overlays Mobile APs ( CoW , UAV) Part 2: URLLC service

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Spatially-temporal traffic dynamics and URLLC service

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  1. Spatially-temporal traffic dynamicsand URLLC service

  2. Outline 2 • 5G services • Applications for 5G NR • Part 1:Servicing spatially-temporal traffic • New AR/VR service characteristics • Mesh-based D2D incentivized overlays • Mobile APs (CoW, UAV) • Part 2:URLLC service • High-end IoT applications • Support in NR • Part 3:Information theory for NOMA • Multiple access and broadcast channel gains • Channel coding for NOMA

  3. 5G services

  4. Envisioned 3GPP 5G Services 4 • Enhanced mobile broadband (eMBB) • Phase 1&2 are over, fully by 2020 • Massive machine-time communications (mMTC) • NB-IoT technology • Ultra-reliable low-latency services (URLLC) • Not yet available and no dates announced…

  5. Service requirements 5 • mMTC • Long-range (link budget 164 dB) • Power efficiency (10+ years battery lifetime) • Extremely dense (1+ million devices/km2) • eMBB • Peak cell rate 10Gbps • Latency at air interface ~10ms • URLLC • Air interface latency <1ms • Probability of delivery 0.99999

  6. 5G: evolution or revolution? 6 • Prior to 5G: just replacing RAT • 5G/5G+ systems are heterogenous in nature • New Radio (NR) RAT (28,38,72 GHz) • Multi-RAT support: LTE/NR/Wi-Fi, etc. • Advanced features: D2D, relays, femto/micro BSs • SDN/NFV capabilities for control plane • NR is expected to support URLLC service • Potential to delivery up to 10GBps per AP • Potential to upper bound latency • Potential to provide reliability • NR brings a lot of new challenges

  7. 5G NR services 7 • NR targets two types of traffic • New bandwidth-greedy services • Virtual reality • Augmented reality • High-rate video applications (FHD+) • High-end IoT applications • Inter-robot communications • V2V communications • Inter-UAV communications

  8. Spatially-temporal traffic dynamics

  9. Conventional traffic 9 Wang, Xu, et al. "Spatio-temporal analysis and prediction of cellular traffic in metropolis." IEEE Transactions on Mobile Computing (2018). • Different time of day

  10. New services: AR, VR 10 • Pokemon Go as an example • Requests do not match topology

  11. Solutions 11 • Static provisioning? • Overprovisioning • Too much CAPEX that may not pay-off… • Better solutions? • Localization (offloading) using D2D technologies • A fraction of traffic can be localized • Offloading onto direct links • On-demand service provisioning • Mobile access points • “Cell-on-wheels” (CoW), UAVs

  12. Traffic localization (offloading)

  13. Localization: support 13 • Do we always need infrastructure?... • D2D technologies • Wi-Fi-direct, LTE-sidelink • 3GPP plans to add NR-direct • What are the issues with D2D technologies? • Limited communications range • Still limited support • Meshes based on D2D?

  14. Localization: D2D meshes 14 • In fact, ad hoc networks • Microwaves – interference • NR could make it possible • Routing is still a challenge… • Incentivization for relaying in a mesh? • Will you spend you resources? • Recall P2P, e.g., BitTorrent • What else? Locher, Thomas, et al. "Free riding in BitTorrent is cheap." (2006).

  15. Localization: blockchain 15 • Blockchain apps in communications

  16. Localization: performance 16 • Operator injects tokens

  17. Mobile APs

  18. Mobile APs: 3GPP support 18 • When content lifetime >> service delivery lifetime • UAV support in 3GPP • As UE and as AP (3GPP TR 36.777, 2018) • NR relaying/sidelink • Planned by 3GPP • Backhauling • IAB TR 38.874, 2018

  19. Mobile APs: exmaple deployment 19 • 600 by 600 meters • N static APs • M UAV APs • NR technology • Clusterized users • Mobile clusters • 100 Ues • Fair scheduling • Solution: particle swarm optimization (PSO) • Wiki: https://en.wikipedia.org/wiki/Particle_swarm_optimization

  20. Mobile APs: example results 20 • Backhaul is critical • Dynamic optimization is critical • Interplay between # of UEs and # of UAVs

  21. URLLC service

  22. High-end IoT applications 22 • Shift in a way we think about IoT end systems • Driven by new use-cases • Driven by electronics evolution

  23. Industrial automation 23 • Autonomous production lines • Traffic characteristics • Low latency (few ms) • High reliability (10-5 BLER) • No enablers so far…

  24. Autonomous driving 24 • Future of car industry • Traffic characteristics • Huge throughput • Low latency • No enablers so far…

  25. Healthcare: remote surgeries 25 • Remote surgeries • Traffic characteristics • Extremely high reliability (<10-9 BLER) • Extremely low latency (<1ms) • No enablers so far… • Da Vinci surgery robot

  26. Healthcare: remote diagnosis 26 • Remote diagnosis example: NR+LTE • Petrov, V., Lema, M. A., Gapeyenko, M., Antonakoglou, K., Moltchanov, D., Sardis, F., and Dohler, M. Achieving end-to-end reliability of mission-critical traffic in softwarized 5G networks. IEEE Journal on Selected Areas in Communications, 36(3), 485-501, 2018.

  27. Addressing latency 27 • Main challenge • NR frame duration: 1ms • Latency < 1ms • How to conform? • Two principal ways • Reservation/priorities • Non-orthogonal multiple access (NOMA) • Intentional overlapping of data • Enabled by flexible NR slot numerology • How to communicate decision to IoT UEs?

  28. Addressing URLLC reliability 28 • Blockage may or may not lead to outage • Case 1: blockage leads to lower MSC scheme • Case 2: blockage leads to outage • Case 1: provide more resources • Bandwidth reservation • Isolated deployments • Case 2: find a new path • 3GPP multi-connectivity • Dense deployments

  29. Reliability: multi-connectivity 29 • Avoiding outage • Gapeyenko, N., Petrov, V., Moltchanov, D., Akdeniz, M., Andreev, S., Himayat, N., Koucheryavy, Y., "On the Degree of 3GPP Multi-Connectivity in Urban 5G Millimeter-Wave Deployments", IEEE Trans. Veh. Tech., 2018.

  30. Reliability: bandwidth reservation 30 • Alleviating lower MCSs • Moltchanov, D., Samuylov, A., Petrov, V., Gapeyenko, M., Himayat, N., Andreev, S., and Koucheryavy, Y. (2018). Improving Session Continuity with Bandwidth Reservation in mmWave Communications. IEEE Wireless Communications Letters, 2018.

  31. What else for URLLC? 31

  32. Information theory for NOMA based on the slides of Dr. I. Pastushok

  33. Independent sources: Receiver: Probabilities of messages at a receiver side: ; ; Uplink MAC channel

  34. Throughput ; Upper Bound on MAC Throughput

  35. Throughput: ; Throughput is not achievable for the independent sources: For example, C is achievablewhen: Upper Bound on MAC Throughput

  36. Rate in Multiple-Access Channel .

  37. . Achievable Regions in MAC

  38. Time Division of MAC (TDMA)

  39. Polar codes allow to achieve channel throughput for BEC channel How to achieve point A

  40. Broadcast Channel BS USERS

  41. Regions for Broadcast Channel

  42. Regions for MAC, BC and TDMA

  43. Channel coding for NOMA 43 • Dai, Jincheng, et al. "Polar coded non-orthogonal multiple access," in Information Theory (ISIT), 2016 IEEE International Symposium on. IEEE, 2016. • Vameghestahbanati, M., et al. "Polar Codes for SCMA Systems," in Vehicular Technology Conference (VTC-Fall), 2017 IEEE 86th. IEEE, 2017. • He, Qingli, et al. "A Nonbinary LDPC-Coded SCMA System with Optimized Codebook Design," in Vehicular Technology Conference (VTC-Fall), 2017 IEEE 86th. IEEE, 2017. • Du, Junyi, et al. “A New LDPC Coded Scheme for Two-User Multiple-Access Channels Aided with Physical-Layer Network Coding,” in Globecom Workshops (GC Wkshps), IEEE, 2017. • Yuan, Lei, et al. “Successive Interference Cancellation for LDPC Coded Non-Orthogonal Multiple Access Systems,” in IEEE Trans. Veh. Tech. 2018. • Chen, Yen-Ming, Wei-Min Lai, and Yeong-LuhUeng, “A Raptor-Coded Distributed Noncoherent Scheme Using Non-Orthogonal Space-Time Modulation,”  in Vehicular Technology Conference (VTC Spring), 2017 IEEE 85th. IEEE, 2017.

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