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Energy efficient radio resource management strategies for green radio

Energy efficient radio resource management strategies for green radio. IET Commun ., 2011, Vol.5, Iss . 18. 2012-03-08 TRINH XUAN DAT. Contents. Introduction System model Energy-aware link adaptation Energy efficiency of SISO and MIMO Proposed MAPR and performance evaluation

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Energy efficient radio resource management strategies for green radio

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  1. Energy efficient radio resource management strategies for green radio IET Commun., 2011, Vol.5, Iss. 18 2012-03-08 TRINH XUAN DAT

  2. Contents • Introduction • System model • Energy-aware link adaptation • Energy efficiency of SISO and MIMO • Proposed MAPR and performance evaluation • Conclusion

  3. 1. Introduction • Green radio: environmentally friendly, low-power and energy efficient solutions for future wireless networks • Link adaptation RRM strategies select Tx parameters according to current channel conditions

  4. Introduction (cont.’) • Low order scheme generally requires less Tx power to maintain a specific quality requirement  LA energy-aware RRM strategies can help to improve energy efficiency by selecting the most energy-saving Tx mode according to current channel conditions • The opportunity to trade off bandwidth for saving energy: allocate free bandwidth to users and allow them to transmit in low spectral efficient modes while maintaining the target data rate

  5. 2. System model • Channel model: spatial channel model extension (SCME) urban macro-scenario

  6. 3. Energy-aware link adaptation • Performance of MCS schemes and overall system of SISO LTE in the SCME channel

  7. 3. Energy-aware link adaptation • The amount of energy saving achieved by a LA scheme is calculated by energy reduction gain (ERG): • ERCREF: energy consumption rate (ERC) of the reference system with Tx power @46dBm • ERCTEST(LA): ERC of the considered system operating at whatever Tx power to match the reference system

  8. Energy-aware link adaptation • Energy consumption rate (ERC) is given by: • P: required average transmit power • R: data rate • T: transmission time over which ERC is calculated • Performance of transmit ERG under various user locations and fixed data rate:

  9. 4. Energy efficiency of SISO and MIMO • Compare energy efficiency of SISO, Alamouti-based space-frequency block coding (SFBC) used in LTE and spatial multiplexing (SM): • Energy consumption for fixed rate requirement, w/o consideration of overhead:

  10. Energy efficiency of SISO and MIMO • Energy consumption for varying traffic load, w/o consideration of overhead:

  11. Energy efficiency of SISO and MIMO • Energy consumption with consideration of overhead:

  12. 5. MRPA scheme • Assumption: overall system traffic is not fully loaded • The proposed Adaptive multi-user resource and power allocation (MRPA) scheme: to minimize total Tx power PT subject to a given user’s rate requirement:

  13. MRPA scheme • Two stages of MRPA: • Stage 1: • Assigns min number of PRBs to meet users’ rate requirement, using the most spectrally efficiency MCS level. Users are assigned PRBs with highest channel gain • Calculate required Tx power of each user Pk • Stage 2: “bandwidth scaling stage” • Find users that are not currently assigned the lowest MSC level • Find user k* that achieves the highest positive power reduction by using a lower MSC scheme through bandwidth expansion until all PRBs are assigned to users

  14. MRPA example • Example of assigning PRBs to users based on MRPA:

  15. Performance evaluation • Compared RRM schemes: • Scheme 1 (used by reference system): fixed scheduling approach – all users transmit at highest MCS level, PRBs are assigned on round-robin approach  not all PRBs are use for transmission • Scheme 2 (“bandwidth expansion” only): all PRBs are assigned to users based on round-robin method • Scheme 3 (multi-user diversity, MUD, only): users transmit at highest MCS level but select best PRBs for transmission

  16. Performance evaluation • Simulation environment: • BS peak Tx rate 43.2Mbps • 5 users: • Distributed uniformly • Each user requests 20% of the overall system load simultaneously • Max range between BS and MS is 300m

  17. Simulation results • In SISO system:

  18. Simulation results • In SISO system:

  19. Simulation results • In MIMO system:

  20. Conclusion • For specified rate requirement, using energy-oriented LA strategy allows system to adapt the most energy efficient MCS to current user’s channel condition to reduce transmission power • Low spectral efficient MCS scheme with low modulation order and stronger coding rate is more energy efficient • The proposed algorithm shows 79%~86% energy reduction • MIMO is more energy efficient than SISO for the same spectral efficiency requirement

  21. Q&A

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