5G As A User-Centric Network. Xiao-Feng Qi

Transcription

1 5G As A User-Centric Network Xiao-Feng Qi

2 Contents 5G at Huawei 5G Vision and Viewpoints A User-Centric View

3 Huawei Began 5G Research since 2009 $600m For 5G Research 300+ Experts 9 Research Centers 2013~2018 By 2014 By 2014 Virtualized RAN SCMA n Virtualized Radio 100Gbps Wireless Full Duplex Massive MIMO 5G Macro (50Gbps) 5G mmwave System MPR-FullDuplex MPR-FullDuplex 50Gbps 50Gbps MPR-FullDuplex Basest Basest ion ion Virtualized 50Gbps Virtualized Basest Radio Radio ion Virtualized 100Gbps Radio Wireless 100Gbps W (100Gbps)

4 Contents 5G at Huawei 5G Vision and Viewpoints A User-Centric View

5 Heterogeneous Service Requirements 4G capability VR AR Media Mobile Broadband Meter Monitor Smart Grid Health Monitor Remote Machinery Public Safety V2V Online Game ecall Surveillance Broadcast 100Mbps 1Gbps 10Gbps High Normal Low Broadband Cost 100ms 10ms 1ms Days Months Years Latency Energy 5km/h 50km/h 500km/h best-effort 95% % Mobility Reliability

6 5G Service Cube

7 NG-MPC The Network of What? for What? D2D/V2V/A2A Wide band LTE, WiFi, New RATs Ultra Dense Service Enabled. Pipe Network of/for Humans and their things User Experience Page 7

8 Contents 5G at Huawei 5G Vision and Viewpoints A User-Centric View

9 Case in Point: Video Streaming Source: Ericsson Mobility Report, MWC 2015 Source: Ericsson Mobility Report, MWC 2015 Channel surfing speed is as important as bit rate Perception of video quality is content dependent Aggregate behavior (e.g. multicast vs. unicast ) Different users have different channel surfing habits, tolerance for perceived QoE, viewing patterns, etc.

10 Service specific User (profile) specific Physiological Physical A User is More Than Just A Dot Psychological Physical Speed and trajectory Distribution pattern Surroundings Network RF placement Device capabilities (HW/SW) Physiological Sensory response time Cadence of exchange Language difference Psychological Content/app preference Service consumption habit Social behavior QoS tolerance P 3 should be learned by both network and devices, as input to a distributed adaptive control loop The extra diversity can lead to higher network efficiency and user satisfaction

11 Service specific Network Impact User Network Enablers Psychological Physiological Physical Mutual Impact RAN Service Domain RRM Scheduling (inc. device resources) User-centric PHY SDN Mobile Edge Network Learning Across the Network

12 User Centric PHY Challenges Virtualized RAN CSIT and other feedback signaling overhead does not scale with network density Front-haul or side-haul capacity limits Solutions Point to Point: UE follows network Group to Group: Network follows user Extract additional user-specific physical information, through machine learning User-adaptive beamforming Joint access and front-haul optimization

13 An Expanded View 5G network can no longer be a resource arbiter indifferent to nuanced user expectations More user centric Customizable objective function, adaptable to dynamic user, service, and network/ue resource availability Uses broader variety of user-specific information for efficient service delivery Intelligent feedback loops Machine learning anticipates user behavior Network supports multi-dimensional user feedback Expanded analytical framework and problem statements Support for new objective functions for network optimization New network scaling laws incorporating user-centric adaptive control loops Cooper vs. Moore Dense network: how to trade signaling overhead for computational complexity? Evolution (or punctured equilibrium) 4G network contains most requisite individual dimensions. Holistic integration is needed (e.g. service layer vs. RAN) Evolution or disruption of device intelligence and interaction can not be overlooked

14 Feedback Loops: From IoH to IoT????? Autonomous mind Programmed mind Mindless or hive mind? Human Network Robot Network Sensor Network

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