1. IEEE VTS UKRI Meeting – EW2013
Toward Energy Efficient 5G
Networks
Mehrdad Dianati
Centre for Communication Systems Research (CCSR)
U i it f SUniversity of Surrey

2. 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

3. Growing Demand for PerformanceG g d
??
• Demand seems to be ever-increasing (exponentially) ….

4. Why energy efficiency is important?
Care for the planet and the “network
operator’s wallet”operator s wallet
Electricity bill is a notable part of
operational expenditure of mobile
operators
Increasing energy cost trends

5. 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

6. Energy and Spectrum Efficiency trade-offgy d Sp y d
Energy
Effi iEfficiency
Spectral
Efficiency?Efficiency?

7. The trade-off (point to point
communication)
Technology
Potential
communication)
dataper
Potential
M th
usefuld
ergy)
Limit for Energy Efficiency
Move there
ciency(u
nitofen
Limit for Energy Efficiency
Possible Improvement?
rgyEffic
un
Possible Improvement?
Ener
A desired performance metric (say
Current Operation
Baseline
A desired performance metric (say
Spectral Efficiency or QoE)

8. Towards Green Networks (1/4)
Deployment
• Deployment scenarios:
optimum cell sizes
Deployment
optimum mix of cell sizes
hierarchical deployments
multi-RAT deploymentsoverlay macro cell
small
cells
relays
EE topology

9. Towards Green Networks (2/4)
• Management algorithms:Management • Management algorithms:
capacity management
multi RAT coordination
Management
multi-RAT coordination
base station sleep mode
t l d iprotocol design
multi-RAT
Zzz
EE adaptive cov./cap.p p

10. Towards Green Networks (3/4)
• RRM algorithms:
RRM
• RRM algorithms:
cooperative scheduling
i t f di tiinterference coordination
joint power allocation and
resource allocationresource allocation
EE j i t RRMEE joint RRM

11. Towards Green Networks (4/4)
• Disruptive approaches:
New Architecture
• Disruptive approaches:
multi-hop transmission
d h t kad-hoc networks
terminal-terminal-
transmission (virtual MIMO)transmission (virtual-MIMO)
cooperative multipoint arch.
EE adaptive backhauling
Adaptive
backhaul
EE adaptive backhauling
cognitive/opportunistic
radios & networksm lti hop radios & networksmulti-hop
Future EE architectures

12. 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

13. 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

14. IEEE VTS UKRI Meeting – EW2013
Energy Efficient Adaptive CoMP
Clustering 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

15. 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.

16. 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.

17. 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.

18. 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

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

20. 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

21. 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.

22. 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)
0.35
0.4
0.45
oule)
Bandwidth
Senario II
Bandwidth
Senario I
0.25
0.3
iency(MBits/Jo
0.25
0.3
ciency(MBits/J
0.15
0.2
EnergyEffic
MIMO (Upper Bound)
Virtual MIMO with CF
(Upper Bound)
Virtual MIMO with CF
0.15
0.2
EnergyEffic
MIMO (Upper Bound)
MIMO (Simulations)
Virtual MIMO with CF
(U B d)
0.05
0.1
Virtual 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 20
0
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) )

23. 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

24. 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. Sabagh
Co-Inventors: M. A. Imran, R. Tafazolli
Centre for Communication Systems Research
University of SurreyUniversity of Surrey

25. 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

26. System Model
Figure 1. System Model
Figure 2. eNodeB Downlink Frame
Page 26

27. General Framework
Scheduling Framework Traffic Model
Page 27

28. Principle Schemes
Page 28

29. Results (1)
20 ti 150 ti20 active users 150 active users
Page 29

30. Results (2)
.
Page 30

31. Results (3)
Page 31

32. 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

33. System Model
Page 33

34. 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

35. 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

36. 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

37. Th k YThank You
Dr Mehrdad DianatiDr. Mehrdad Dianati
m.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.