ieee vts talk

IEEE VTS UKRI Meeting – EW2013

Toward Energy Efficient 5G NetworksMehrdad Dianati

Centre for Communication Systems Research (CCSR) U i it f SUniversity of Surrey

Agendag

• Background/Motivations

• Key research areas that will affect energy efficiency of the future networks.efficiency of the future networks.

Energy efficiency research in CCSR• Energy efficiency research in CCSR

– Past and current projects– Highlights of the results

Growing Demand for PerformanceG g d

??

• Demand seems to be ever-increasing (exponentially) ….

Why energy efficiency is important?

Care for the planet and the “networkoperator’s wallet”operator s wallet

Electricity bill is a notable part of operational expenditure of mobile operators

Increasing energy cost trends

Dividing Energy Consumption of Access Networks

Gateway (PDG GGSN)

Base Station Network Server

(SGSN, HLR)Internet

Access NetworkMobile Core Network

(PDG, GGSN)

Media Server (IMS)

70-80% 2-10%10-20%Energy Consumption

(CO2-contribution)( 2 )

CCSR’s main focus

Energy and Spectrum Efficiency trade-offgy d Sp y d

Energy Effi iEfficiency

Spectral Efficiency?Efficiency?

The trade-off (point to point communication)

Technology Potential

communication)da

ta p

er Potential

M th

usef

ul d

ergy

)

Limit for Energy Efficiency

Move there

cien

cy (

uni

t of

en Limit for Energy Efficiency

Possible Improvement?

rgy

Effic un

Possible Improvement?

Ener

A desired performance metric (say

Current OperationBaseline

A desired performance metric (say Spectral Efficiency or QoE)

Towards Green Networks (1/4)

Deployment• Deployment scenarios:

optimum cell sizes

Deployment

optimum mix of cell sizeshierarchical deploymentsmulti-RAT deploymentsoverlay macro cell

smallcells

relays

EE topology

Towards Green Networks (2/4)

• Management algorithms:Management • Management algorithms:capacity managementmulti RAT coordination

Management

multi-RAT coordinationbase station sleep mode

t l d iprotocol designmulti-RAT

Zzz

EE adaptive cov./cap.p p

Towards Green Networks (3/4)

• RRM algorithms:RRM • RRM algorithms:cooperative schedulingi t f di tiinterference coordinationjoint power allocation and resource allocationresource allocation

EE j i t RRMEE joint RRM

Towards Green Networks (4/4)

• Disruptive approaches:New Architecture

• Disruptive approaches:multi-hop transmissiond h t kad-hoc networks

terminal-terminal-transmission (virtual MIMO)transmission (virtual-MIMO)cooperative multipoint arch.EE adaptive backhauling

Adaptivebackhaul

EE adaptive backhaulingcognitive/opportunistic radios & networksm lti hop radios & networksmulti-hop

Future EE architectures

Energy Efficiency Research in CCSR

• CCSR has been one of the pioneers of EE research:

– MVCE Green Radio

– EU-FP7 EARTH Projectj

– Huawei Green Comms. Projectj

Huawei Green Comms Project

• Funded by Huawei Technologies

• Work areas:– Fundamental aspects of energy efficiency inFundamental aspects of energy efficiency in

communication systems– Massive MIMO for energy efficient communicationsgy– Energy efficient RRM– CoMP techniques for energy efficiencyCoMP techniques for energy efficiency– Multi-RAT solutions

IEEE VTS UKRI Meeting – EW2013

Energy Efficient Adaptive CoMPClustering for LTE-A Systems

Efstathios Katranaras, M. A. Imran, M. Dianati

C t f C i ti S t R hCentre for Communication Systems Research University of Surrey

Background & Problem Overview

• The aim is to coordinate Inter-cell interference (ICI)( )

• The approach is Coordinated Multi Point Joint Transmission (CoMP-JT)

• In practice, only clustered CoMP deployments are feasible due to the signalling overheadsignalling overhead

• The existing studies mostly consider static clustering schemes

• We study adaptive clustering for LTE-A systems.

Basic Idea

Dynamically adjust the size and the configuration ofDynamically adjust the size and the configuration of the clusters. The clustering is adapted according to the network load and other propagation factors.

Main Results (1)Main Results (1)Comparing clustering schemes in terms of achieved average EE per UE for

various UEs-snapshots.p

Algorithms based on the proposed framework are robust to the changes of the

physical environmentphysical environment.

Main Results (2)Main Results (2)CDF of per-UE EE for various clustering schemes.

No significant EE degradation for all UEs = Minimising energy waste for UEs

th t i i ifi t i d t tithat experience no significant gain due to cooperation

Main Results (3)Main Results (3)Average EE per cell for various clustering schemes.

IEEE VTS UKRI Meeting – EW2013

EE Analysis and Optimization of Virtual-MIMO Systems

Jing Jiang, M. Dianati, M. A. Imran

C t f C i ti S t R hCentre for Communication Systems Research University of Surrey

EE Analysis and Optimization of Virtual-MIMO Systemsy

• Main Contributions:– An upper bound for EE as a function of SE

– Optimal power allocation, bandwidth ll ti b f t it t dallocation, number of transmit antennas, and

cooperating nodes.

EE Analysis and Optimization of Virtual-MIMO Systems

0 45

0.5

0.45

0.5

Virtual MIMO Systems

0.35

0.4

0.45

oule

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0.35

0.4

0.45

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)Bandwidth Senario II

BandwidthSenario I

0.25

0.3

ienc

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MB

its/J

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0.25

0.3

cien

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MB

its/J

0.15

0.2

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MIMO (Upper Bound)

Virtual MIMO with CF(Upper Bound)

Virtual MIMO with CF

0.15

0.2

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rgy

Effi

c

MIMO (Upper Bound)

MIMO (Simulations)

Virtual MIMO with CF(U B d)

0.05

0.1Virtual MIMO with CF(Simulations)

Virtual MIMO with AF(Upper Bound)

Virtual MIMO with AF(Simulations)

0.05

0.1(Upper Bound)

Virtual MIMO with CF(Simulations)

MISO (Upper Bound)

MISO (Simulations)

0 5 10 15 200

Spectral Efficiency (bits/s/Hz)

(b)

0 5 10 15 20

Spectral Efficiency (bits/s/Hz)

(a)EE performance of the 2-by-2 virtual-MIMO system with G=10dB (Bandwidth scenario I is considered in (a) and Bandwidth(Bandwidth scenario I is considered in (a) and Bandwidth scenario II is in (b) )

EE Analysis and Optimization of Virtual-MIMO Systemsy

• Main Conclusions:Th lt d t t th t h SE i l EE i– The results demonstrates that when SE is low, EE is dominated by the load-independent circuit power.

– As SE increases, transmit power contributes more to the EE performancethe EE performance.

Compared to the ideal MIMO system virtual MIMO– Compared to the ideal MIMO system, virtual-MIMO system requires more energy for the cooperation, but outperforms the non-cooperative MISO.p p

IEEE VTS UKRI Meeting – EW2013

B ff A d E Effi i tBuffer Aware and Energy Efficient Scheduling of Real Time Traffic for OFDMA

Systems

Inventors: M. Dianati, M. SabaghCo-Inventors: M. A. Imran, R. Tafazolli

Centre for Communication Systems Research University of SurreyUniversity of Surrey

Background & Prior Techniques

• The existing scheduling scheme are designed toThe existing scheduling scheme are designed to optimise spectral efficiency for operators and maintain QoS for users (see attached document).

• The aim is to propose energy efficient packet scheduling for real time traffic in OFDMA systems.

Page 25

System Model

Figure 1. System Model Figure 2. eNodeB Downlink Frame

Page 26

General Framework

Scheduling Framework Traffic Model

Page 27

Principle Schemes

Page 28

Results (1)

20 ti 150 ti20 active users 150 active users

Page 29

Results (2)

.

Page 30

Results (3)

Page 31

IEEE VTS UKRI Meeting – EW2013

I t f S ti l C l ti EE fImpacts of Spatial Correlation on EE of massive-MIMO Systemsy

Jing Jiang, M. Dianati, M. A. Imran

C t f C i ti S t R hCentre for Communication Systems Research University of Surrey

System Model

Page 33

Results and Discussion

EE simulations and UBs for Rayleigh-fading MIMO channels (Constant spatial correlation with φt = φr = 0.5 is considered in (a), and the results for i.i.d. fading channels are in (b).)

Page 34

Results and Discussion

The relation between EE and SE for exponentially correlated MIMO channels and φ = φ = 0 5 (The effects of loadMIMO channels and φt = φr = 0.5 (The effects of load-independent circuit power on EE are also shown.)

Page 35

Simulation Results and Discussion

The EE performance as a function of coefficient φ (where φt=φ =φ) for both constant and exponential correlated Rayleigh=φr=φ) for both constant and exponential correlated Rayleigh-fading MIMO channels at RM = 20 bits/s/Hz.

Page 36

Th k YThank YouDr Mehrdad DianatiDr. Mehrdad Dianatim.dianati@surrey.ac.uk

Acknowledgement: Dr. M. A. Imran, Dr. E. Katranars, Dr. J. Jiang, Mr. M. Sabagh, and Dr. Amir Akbari have contributed to the technical work and the preparation of the slidestechnical work and the preparation of the slides

CONFIDENTIAL,  EARTH Project.