Ieee vts talk

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Ieee vts talk

  1. 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. 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. 3. Growing Demand for PerformanceG g d ?? • Demand seems to be ever-increasing (exponentially) ….
  4. 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. 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. 6. Energy and Spectrum Efficiency trade-offgy d Sp y d Energy Effi iEfficiency Spectral Efficiency?Efficiency?
  7. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 19. Main Results (3)Main Results (3) Average EE per cell for various clustering schemes.
  20. 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. 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. 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. 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. 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. 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. 26. System Model Figure 1. System Model Figure 2. eNodeB Downlink Frame Page 26
  27. 27. General Framework Scheduling Framework Traffic Model Page 27
  28. 28. Principle Schemes Page 28
  29. 29. Results (1) 20 ti 150 ti20 active users 150 active users Page 29
  30. 30. Results (2) . Page 30
  31. 31. Results (3) Page 31
  32. 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. 33. System Model Page 33
  34. 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. 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. 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. 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. 

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