基于能量消耗和负载均衡的异构网络基站开闭策略研究
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摘要
提出新的节能机制是异构网络的一个重大挑战.针对可开闭异构网络,提出了基于纳什均衡的离散粒子群算法(NP-DPSO算法),旨在通过本算法综合考虑系统能量消耗与负载,达到均衡状态尽可能节省系统能量.基站仅需根据自身的能量消耗和负载来决定自身的开闭状态,而勿需根据全局信息,以用户和基站为博弈参与方建立博弈模型,博弈方均以自身利益最大化为目标执行自己的策略,并在每个参与者都认为已经做出了最优策略的情况下达到非合作博弈的均衡点(纳什均衡点),再利用离散粒子群算法对其求解.通过将NP-DPSO算法与基站全部激活状态(BSAA状态)、休眠激活机制的概率唤醒算法(APO算法)和基于基站休眠的负载合并动态功率控制算法(LDPC算法)进行了对比分析,验证本文提出的NP-DPSO算法的先进性与可行性.
Proposing a new energy-saving mechanism for heterogeneous networks is a major challenge. For switchable heterogeneous network,this study proposed a discrete particle swarm algorithm based on the decision Nash equilibrium(NP-DPSO algorithm). This algorithm considers the energy consumption and load of the system comprehensively to reach the equilibrium state and save system energy as much as possible. The base station only needs to determine its state based on its own energy consumption and load,without the need for global information. A game model is established between the user and the base station as game participants. The game players execute their own strategies based on their own maximized interests and reach a non-cooperative game in which each participant believes that they have made the best strategy. The equilibrium point(Nash equilibrium point)is solved by using a discrete particle swarm algorithm. By comparing the NP-DPSO algorithm with the total active state of the base station,probabilistic wake-up algorithm of the sleep activation mechanism algorithm and load-based dynamic power control algorithm based on base station hibernation,this study verified the feasibility and advantages of the proposed NP-DPSO algorithm.
Proposing a new energy-saving mechanism for heterogeneous networks is a major challenge. For switchable heterogeneous network,this study proposed a discrete particle swarm algorithm based on the decision Nash equilibrium(NP-DPSO algorithm). This algorithm considers the energy consumption and load of the system comprehensively to reach the equilibrium state and save system energy as much as possible. The base station only needs to determine its state based on its own energy consumption and load,without the need for global information. A game model is established between the user and the base station as game participants. The game players execute their own strategies based on their own maximized interests and reach a non-cooperative game in which each participant believes that they have made the best strategy. The equilibrium point(Nash equilibrium point)is solved by using a discrete particle swarm algorithm. By comparing the NP-DPSO algorithm with the total active state of the base station,probabilistic wake-up algorithm of the sleep activation mechanism algorithm and load-based dynamic power control algorithm based on base station hibernation,this study verified the feasibility and advantages of the proposed NP-DPSO algorithm.
引文
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[13]朱喜华,李颖晖,李宁,等.基于改进离散粒子群算法的传感器布局优化设计[J].电子学报,2013,41(10):2104-2108.Zhu Xihua,Li Yinghui,Li Ning,et al.Optimization design of sensor layout based on improved discrete particle swarm optimization algorithm[J].Chinese Journal of Electronics,2013,41(10):2104-2108(in Chinese).
[2]Damnjanovic A,Montojo J,Wei Y,et al.A survey on3GPP heterogeneous networks[J].IEEE Wireless Communications,2011,18(3):10-21.
[3]Zhang H,Chen S,Feng L,et al.A universal approach to coverage probability and throughput analysis for cellular networks[J].IEEE Transactions on Vehicular Technology,2015,64(9):4245-4256.
[4]Feng D,Jiang C,Lim G,et al.A survey of energyefficient wireless communications[J].IEEE Communications Surveys&Tutorials,2013,15(1):167-178.
[5]张高记,吕建东,陈文学.通信基站节能减排评估指标及评估方法研究[J].电信科学,2011,27(3):101-105.Zhang Gaoji,LüJiandong,Chen Wenxue.Research on evaluation indexes and evaluation methods for energy saving and emission reduction of communication base stations[J].Telecommunication Science,2011,27(3):101-105(in Chinese).
[6]向伟,张剑峰,邵鑫鸿,等.基于基站休眠的负载合并动态功率控制算法[J].通信技术,2015,48(5):555-559.Xiang Wei,Zhang Jianfeng,Shao Xinhong,et al.Load-based dynamic power control algorithm based on base station hibernation[J].Communications Technology,2015,48(5):555-559(in Chinese).
[7]Son K,Kim H,Yi Y,et al.Base station operation and user association mechanisms for energy-delay tradeoffs in green cellular networks[J].IEEE Journal on Selected Areas in Communications,2011,29(8):1525-1536.
[8]Zhou S,Goldsmith A J,Niu Z.On optimal relay placement and sleep control to improve energy efficiency in cellular networks[C]//IEEE International Conference on Communications.Kuala Lumpur,Malaysia,2011:1-6.
[9]Yong S S,Quek T Q S,Kountouris M,et al.Energy efficient heterogeneous cellular networks[J].IEEE Journal on Selected Areas in Communications,2013,31(5):840-850.
[10]Koudouridis G P,Li H.Distributed power on-off optimisation for heterogeneous networks:A comparison of autonomous and cooperative optimization[C]//IEEE,International Workshop on Computer Aided Modeling and Design of Communication Links and Networks.Barcelona,Spain,2012:312-317.
[11]Luo Z,Ding M,Luo H.Dynamic small cell on/off scheduling using stackelberg game[J].IEEE Communications Letters,2014,18(9):1615-1618.
[12]马晓彤,金顺福,刘建平,等.认知无线电网络中的基站节能策略及纳什均衡研究[J].通信学报,2016,37(7):172-181.Ma Xiaotong,Jin Shunfu,Liu Jianping,et al.Research on base station energy saving strategy and nash equilibrium in cognitive radio networks[J].Journal on Communications,2016,37(7):172-181(in Chinese).
[13]朱喜华,李颖晖,李宁,等.基于改进离散粒子群算法的传感器布局优化设计[J].电子学报,2013,41(10):2104-2108.Zhu Xihua,Li Yinghui,Li Ning,et al.Optimization design of sensor layout based on improved discrete particle swarm optimization algorithm[J].Chinese Journal of Electronics,2013,41(10):2104-2108(in Chinese).