小蜂窝认知网络中基于干扰和能量效率的动态频谱管理
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摘要
针对基于非授权频段的小蜂窝认知网络中的抑制干扰和降低功耗问题,提出了干效性的概念,即传输每比特数据所产生的干扰.建立基于能效性和干效性的多目标收益函数,以最大化长期折扣收益为目标,利用随机动态优化理论中基于马尔科夫决策过程的Restless Bandits模型,计算其最优频谱管理决策,给出了动态频谱管理决策的过程.该方法具有分布式和动态特性且计算复杂度较低.仿真结果表明该方法能显著提高系统收益能效性、干效性等性能.
With the introduction of interference efficiency(IE),which represents the interference introduced by the transmission of each data bit,an optimal dynamic spectrum management scheme was proposed to deal with the problem of reducing both interference and power consumption in small-cell cognitive radio networks working on unlicensed spectrum bands.Both the multi-objective reward function with energy and interference efficiency and spectrum management decisions to maximizing their long-term discounted reward were established and optimized by the cognitive radio base stations in the small cells.The restless bandits problem based on the Markov decision process in the dynamic stochastic optimization theory was used for the optimization schemes,and the process of dynamic spectrum management was presented.The proposed scheme has low computation complexity with distributed and dynamic features.Simulation results are provided to demonstrate the significant performance improvement of the proposed scheme.
With the introduction of interference efficiency(IE),which represents the interference introduced by the transmission of each data bit,an optimal dynamic spectrum management scheme was proposed to deal with the problem of reducing both interference and power consumption in small-cell cognitive radio networks working on unlicensed spectrum bands.Both the multi-objective reward function with energy and interference efficiency and spectrum management decisions to maximizing their long-term discounted reward were established and optimized by the cognitive radio base stations in the small cells.The restless bandits problem based on the Markov decision process in the dynamic stochastic optimization theory was used for the optimization schemes,and the process of dynamic spectrum management was presented.The proposed scheme has low computation complexity with distributed and dynamic features.Simulation results are provided to demonstrate the significant performance improvement of the proposed scheme.
引文
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[2]NI W,COLLINGS I B.A new adaptive small-cell architecture[J].IEEE Journal on Selected Areas in Communications,2013,31(5):829-839.
[3]WILDEMEERSCH M,QUEK T Q S,RABBACHIN A,et al.Energy efficiency design of cognitive small cells[C]//Proc IEEE ICC.Budapest:Hungary,2013:2701-2706.
[4]ASHRAFI,BOCCARDIF,HOL.Sleep mode techniques for small cell deployments[J].IEEE Communications Magazine,2011,49(8):72-79.
[5]HUANG L,ZHOU Y Q,HAN X.Distributed coverage optimization for small cell clusters using game theory[C]//Proc IEEE WCNC.Shanghai:IEEE,2013:2289-2293.
[6]BENNIS M,PERLAZA S M,BLASCO P.Selforganization in small cell networks;a reinforcement learning approach[J].IEEE Transactions on Wireless Communications,2013,12(7):3202-3212.
[7]MASO M,CARDOSO L S,DEBBAH M.Channel estimation impact for LTE small cells based on MU-VFDM[C]//Proc IEEE WCNC.Paris:IEEE,2012;2560-2565.
[8]WILDEMEERSCH M,QUEK T Q S.Cognitive small cell networks:energy efficiency and trade-offs[J].IEEE Transactions on Communications,2013,61(9):4016-4029.
[9]ELSAWY H,HOSSAIN E.HetNets with cognitive small cells:user offloading and distributed channel access techniques[J].IEEE Communication Magazine,2013,51(6):28-36.
[10]LIU Y K,CAI L X.Deploying cognitive cellular networks under dynamic resource management[J].Wireless Communications of IEEE,2013,20(2):82-88.
[11]WHITTLE P.Restless bandits;activity allocation in a changing world[J].Journal of Applied Probability,1988,25(1):287-298.
[12]BERTSIMAS D,NINO-MORA J.Restless bandits,linear programming relaxations,and a primal-dual index heuristic[J].Operations Research,2000,48(1):80-90.
[13]SI P B,FRICHARD Y,ZHANG Y H.Optimal transmission behavior policies of secondary users in proactive-optimization cognitive radio networks[J].China Communications,2013,10(8):1-17.
[14]HAN C,ARMOUR S.Energy efficient radio resource management strategies for green radio[J].IET Communications,2011,18(5):2629-2639.
[15]XU Y Y,XU X D,LI Y,et al.Energy-efficient power control for fractional frequency reuse[C]//Proc IEEE PIMRC.London:IEEE,2013:2962-2966.
[16]HANSAN Z,BOOSTANIMEHR H,BHARGAVA V K.Green cellular networks:a survey,some research issues and challenges[J].IEEE Communications Surveys&Tutorials,2011,13(4):524-540.
[17]SI P B,FRICHARD Y,JI H,et al.Optimal cooperative internetwork spectrum sharing for cognitive radio systems with spectrum pooling[J].IEEE Transactions on Vehicular Technology,2010,59(5):1760-1768.