Introduction to The EPSRC SERAN Project

Transcription

1 Introduction to The EPSRC SERAN Project John Thompson 2, Simon Armour 1, Tim O Farrell 3 1 Electrical and Electronic Engineering Dept University of Bristol 2 Institute for Digital Communications The University of Edinburgh 3 Department of Electronic & Electrical Engineering University of Sheffield

2 Outline Introduction Human Centric Data Traffic Modelling Channel Modelling and Network Deployment Flexible Resource Management Outlook to Future Wireless Networks 2

3 SERAN Project Title: Seamless and Adaptive Wireless Access for Efficient Future Networks (SERAN) SERAN proposal targets: New Radio Access Networks (RANs) providing higher capacity to meet traffic demand; Novel algorithms and protocols which enable the effective use of microwave and millimetre wave frequencies in heterogeneous networks, while minimizing interference; Demonstration of concepts which can deliver seamless connectivity along with improved energy efficiency. 3

4 SERAN Research Team University of Bristol University of Edinburgh University of Sheffield PI: Simon Armour PI: John Thompson PI: Tim O Farrell CoI: Andy Nix CoI: Harald Haas CoI: Charith Abhayaratne 4

5 Future Heterogeneous Networks Base Station Microwave Frequency: Macro Cells Mm-wave Hotspot Terminals Wifi Hotspot Millimetre Wave Frequency: Hotspot Millimetre Wave Frequency: Direct Comms 5

6 Microwave vs Millimetre Wave Parameter Microwave Mm-Wave Propagation Wide-Area Short Range Bandwidth Tens MHz Several GHz Beamforming RF Technology Spectrum Allocations Propagation Attenuation Effects Desirable at Base Station to increase capacity Mature manufacturing capability 800, 900 MHz, 1.8 and 2 GHz Line of Sight and Non Line of Sight Good diffraction and relection properties Essential to mitigate increased path-loss Currently for spec-ialist applications TBD Initially in GHz Band Predominantly Line of Sight Blockages a major issue; Rain and oxygen absortion 6

7 Human Centric Data Traffic Population distribution Heterogeneous data demands The population of Manhattan, New York, distribution in space and time. 7 Source:

8 Illustration of 3W-factor The role of the 3W-factor in data demand distributions Who When Video A user s mobile use depends on his/her gender, age, occupation & habits; Up-to 80% of cellular data traffic happens either at home or at work; The evening cellular-data peak is disappearing coinciding with the Wi-Fi-peak usage taking over the previous cellular peak hour. 8 Source: P. Cao.et.al, Human Centric Deployment and Resource Management for Heterogeneous Dense Networks, submitted to IEEE Communication Magazine, 2017 [Joint work between UoE, UoB, UoS]

9 Channel Modelling Ray-capture and Handover example Ray Tracing Approach using Prophecy Engine BS1 BS2 Video 9

10 Path-Loss Modelling For HetNets, need to understand radio propagation at microwave and millimetre wave frequencies simultaneously Based on a sophisticated 3D ray-tracing propagation tool developed by the University of Bristol (UoB) LTE-800MHz channels mm-wave 60GHz channels 10 Source: A. Goulianos, et.al, Large-Scale Modeling and Cell-Edge Coverage for Future HetNet Deployments, IEEE VTC, 2016.

11 Path-Loss Modelling PL(d) = P n log_10(d/d0) + Xa [db] n: the path-loss exponent; d: distance; Xa: the shadow fading term ~CN(0,a) Table 1. Large scale parameters and cell-edge coverage for LTE and mm-wave cells. Cell Type Path-loss exponent (n) Reference distance d0 (meters) Standard deviation of shadowing a (db) Cell-edge coverage (meters) LTE-800MHz LTE-2.6GHz mm-wave (Dense urban) mm-wave (Street level) *(d-d0) *(d-d0) 180 Source: A. Goulianos, et.al, Large-Scale Modeling and Cell-Edge Coverage for Future HetNet Deployments,IEEE VTC,

12 Network Deployment Estimate the human centric data traffic distribution map; Divide the area into multiple sub-areas according to their data traffics; Plan different types of cells for different sub-areas according to their corresponding maximum peak data traffic and cells capacity and coverage. 12 Source:P. Cao.et.al, Human Centric Deployment and Resource Management for Heterogeneous Dense Networks, submitted to IEEE Communication Magazine, 2017 [Joint work between UoE, UoB, UoS]

13 Energy Impact for 5G Wireless The energy consumption of wireless networks is contributing to global carbon emissions: 16 Exabytes per month by 2018 Human centric data flows in networks are also found to be very non-uniform which suggests significant energy savings are possible Reducing the energy consumption to lower carbon emissions and expense is crucial for future 5G communication systems. Source: Ericsson 13

14 Energy Comparison Consider performance metrics when comparing a reference system (REF) and proposed system (PRO): 1. Energy Consumption Gain (ECG) ECG = Power_ref/Power_pro Here ECG > 1 means Energy Gain, < 1 means Energy Loss 2. Date Rate Gain (DVG) DVG = Rate_pro/Rate_ref Here DVG > 1 means Data Rate Gain 3. Energy Efficiency Gain (EEG) EEG = ECG DVG 14

15 Energy Comparison Numerical Example Setting: Cell density: 1-40cells/km 2 ; DVG: Reference network: macro-cells only with a deployment density of cell/km Greater DVG results in more energy consumption; 2. Energy efficiency: Pico-cell only networks > HetNet of macro-and pico-cells > HetNet of macro- and micro-cells. 3. Pico-cell only networks support the greatest DVGs. 15 Source: A. Arbi, et.al, Toward Green Evolution of Cellular Networks by High Order Sectorisation and Small Cell Densification, book chapter, IGI Global, Hershey, PA, 2017.

16 Network Power Minimization Problem: Minimize Total network power, Subject to: Per-UE rate constraint, Transmit Power constraint. Sub-problems: 1. BS-UE association 2. Frequency bands assignment 3. BS sleeping 4. Multi-connectivity 5. Interference management 16

17 Network Slicing + Semidynamic Optimization based on large scale channel fading (long-term) Local beam-forming based on dynamic channel fading 17 Source:P. Cao.et.al, Semidynamic green resource management in Downlink Heterogeneous networks by group sparse power control, IEEE JSAC, 2016.

18 Network Power Minimization Numerical Example: 10 pico-cells and 6 UEs in each macro-cell and each macro-bs and pico-bs possess 8 and 4 antennas, respectively Data Rate Gain = 1 for all curves 18 FRM achieves ECG of 3 vs Transmit Power Minimization ( Min. T-Power ) and 1.2 vs only switching on/off BSs ( On/off only BS ). Source:P. Cao.et.al, Semidynamic green resource management in Downlink Heterogeneous networks by group sparse power control, IEEE JSAC, 2016.

19 Key Features of 5G and Beyond 1. Big data driven network optimization will be important 2. Micro-wave and mm-wave hybrid operation (multiconnectivity) To meet very high date rate demands 3. Network security will become increasingly important 4. Support Cyber-physical systems such as Autonomous Cars 5. Smart Cities: combining with the Internet of Things

20 20 Thank you for your attention!