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Yue Wang: AI in 5G –the Why and How - Jan 2019

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Presentation by Yue Wang, Samsung Research UK at IET 5G - the Advent conference on 30 January 2019 | IET London: Savoy Place

This presentation looks scalable and deployable AI solution in the 5G Infrastructure along with updates from ETSI. Use cases such as Energy Saving, Cell Selection, Fronthaul Management, Orchestration & more are discussed in this presentation.

*** SHARED WITH PERMISSION ***

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Yue Wang: AI in 5G –the Why and How - Jan 2019

  1. 1. AI in 5G – the Why and How Yue Wang, Samsung Research UK 5G - the Advent, 30 Jan, 2019
  2. 2. What has changed in 5G? 2 • Smart phones vs cars, cameras… • End customers vs. Verticals - B2B, B2B2C B2B2X • Best efforts vs customised SLA
  3. 3. What has changed in 5G? 3 • One Network (with new technologies) • Network • SBA • Slicing • Access - 5GNR • New spectrum • massive MIMO • numerology • Deployment – SA/NSA A complicated network RAN, TRANSPORT, CORE ORCHESTRATOR
  4. 4. How Can AI Help? 4 • High efficiency • Context aware • Future proof AI • Scalability • Adaptability • Flexibility • Real-time • Dynamicity • Flexible and scalable to support variety technologies and KPIs • Fast adaptable to new (sometimes real time) network contexts • Optimised operations and efficiently use of resources – OPEX 5G Better Service, More customers, with Lower Cost
  5. 5. Use Cases from ENI 5
  6. 6. AI for Energy Saving 6 Configuration for peak hour * ETSI ENI • Servers take 70% of the total power consumption • Deployed and running to meet the requirement of peak hour service - 100% powered-up full time • Learn and update service pattern • Autonomously turn spare servers to idle state • Predict peak hours and wake up the necessary number of servers
  7. 7. AI for Cell Selection UE Actions (the selected TRP) cell selection with AI UE location, speed, measured signal strengths (RSRP/RSRQ) Feedbacks from the network UE 7 36.304, cell reselection procedure: • 35 parameters for system information • 10 parameters for speed dependent selection • 13 parameters for interworking • The list is getting larger: • New technologies - beam sweeping • New services Without AI With AI Periodically measured Triggered measurement Threshold based (lots of parameters/ configurations) No threshold Static configurations Real-time, adapted to changes of the context (e.g., speed) Faster selection Reduced latency
  8. 8. AI for Fronthaul Management 8 • Multiple factors • Changing contexts • Large dimensionality of solution space • Flexible and dynamic resource slicing and functional split • Real-time optimisation RAU Fronthaul Cluster FronthaulOrchestration andManagement Load Estimation RCC Data-plane Fronthaul traffic per slice Functional split, cluster size designation and resource reservation Low MAC High PHY Low PHY RF PDCP RLC High MAC
  9. 9. Elastic Resource Management Elastic Intra-slice Orchestration Elastic Cross-slice Orchestration A B C D E F Network Control Network Orchestration Multi-slice awareness Single slice Optimization VNF elasticity • Computational aspects of VNFs • Orchestration of the computational resources across slices • Optimise VNF migration using intelligence on multiple resource utilization data (CPU, RAM, storage, bandwidth) • Elastic resource provisioning to network slices *5G-MoNArch *ETSI ENI
  10. 10. Putting Together 10 Core Network/Cloud NFV RCC Orchestrator AI AI AI AI VNFs AI AI AI AI Initial Access Fronthaul management Slice Management AI Power management
  11. 11. But there is more… 11 Service orchestration and management • Service deployment • Service optimisation and prioritisation • Service assuranceRAN Transport Core AI
  12. 12. Problem Domain 12 Simple AI for self- contained problems Local contexts Immediate benefit Trickier AI for linked problems Network contexts Compatibility and enhancement on the E2E network New problems/systems with advanced AI Significant change of the system with advanced AI (Neural network, deep learning) Disruptive innovations, longer term OR just for the fun of research
  13. 13. In the AI space… 13 Artificial Intelligence Machine learning Reinforcement learning Classification/regression … … Supervised learning clustering … … Un-Supervised learning Abnormality detection … Knowledge representation and reasoning Natural language processing Multi-agent systems Robotics
  14. 14. What it should be 14 What it actually is
  15. 15. Think more on the practical side 15 Put an ML in the network Results/enhancement • Synthesized data? • Accurate representation of real data? • How is one ML better than the other? • The integration to the network? • Isolated AI? • Network contexts? • The inter-link of the network? A scalable and deployable AI solution How to turn concept to practice? Open questions • Specific data for specific solutions vs unified data • Standardization vs open source • Integration and compatibility to the (‘legacy’) network
  16. 16. Thank You yue2.wang@samsung.com @YUEWUK

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