认知无线电中一种新型的功率控制算法

doi:10.16182/j.issn1004731x.joss.201707029

摘要/Abstract

摘要： 针对在认知无线电网络通信过程中的远近效应、路径损耗过大以及算法稳定性等问题,提出了一种新的认知无线电网络功率控制算法。该算法对效用函数进行了改进,加入链路质量和主用户干扰容限值等惩罚因素,将所有认知用户的信干噪比控制在最小阈值以上,同时将认知用户的总干扰值控制在主用户的干扰容限值之内。证明了该算法纳什均衡的存在性和唯一性。MATLAB仿真结果表明,该算法稳定性较好,在使用较小发射功率的情况下,可以减少路径的损耗,并有效地解决了远近公平效应,同时,使得认知用户的干扰总值小于主用户的干扰容限。

关键词: 认知无线电, 功率控制, 效用函数, 远近效应, 路径损耗

Abstract: For the problems of near-far effect, large path loss, and stability of algorithm, etc., in the cognitive radio, a new power control algorithm was proposed. Utility function was improved effectively, where punishment factors, such as, link quality and the interference capacity limit of the primary users (PUs) were considered. Signal-to-interference radio (SINR) of cognitive users (CUs) was controlled above the minimum threshold, and the interference value of the CUs was controlled below the capacity tolerance of PUs. The existence of Nash equilibrium was given. Simulation results show that the new algorithm has better stability; the loss of path can be reduced with smaller transmission power; the near-far effect problem can be solved effectively, and the total interference of CUs is less than the interference capacity tolerance of PUs.

Key words: cognitive radio, power control, utility function, near-far effect, path loss

中图分类号:

TN915.5

龙敏, 雷娟娟. 认知无线电中一种新型的功率控制算法[J]. 系统仿真学报, 2017, 29(7): 1617-1624.

Long Min, Lei Juanjuan. Novel Power Control Algorithm for Cognitive Radio Network[J]. Journal of System Simulation, 2017, 29(7): 1617-1624.

参考文献

[1] Berlemann L, Hiertz G R, Walke B, et al.Strategies for distributed QoS support in radio spectrum sharing[C]// IEEE International Conference on Communications. IEEE. USA: IEEE, 2005: 3271-3277.
[2] De S C F, Abbas-Turki M, Abou-Kandil H, et al. Transmission Power Control for Opportunistic QoS Provision in Wireless Networks[J]. IEEE Transactions on Control Systems Technology (S1063-6536), 2013, 21(2): 315-331.
[3] 张莹, 滕伟, 韩维佳, 等. 认知无线电频谱感知技术综述[J]. 无线电通信技术, 2015, 41(3): 12-16.
(Ying Z, Wei T, Weijia H, et al.Overview of cognitive radio spectrum sensing technology[J]. Radio Communication Technology (S1003-3114), 2015, 41(3): 12-16.)
[4] Nekouei E, Inaltekin H, Dey S.Power Control and Asymptotic Throughput Analysis for the Distributed Cognitive Uplink[J]. IEEE Transactions on Communications (S0090-6778), 2014, 62(1): 41-58.
[5] 余翔, 张小银, 刘磊. 基于归一化效用函数的功率控制算法研究[J]. 电信科学, 2014, 30(12): 71-75.
(Xiang Y, Xiaoyin Z, Lei L.Power Control Algorithm Based on Normalized Utility Function Research[J]. Telecommunications Science, 2014, 30(12): 71-75.)
[6] 程世伦, 杨震, 张晖. 基于认知无线电系统的新型合作功率控制博弈算法[J]. 通信学报, 2007, 28(8): 54-60.
(Shi-lun C, Zhen Y, Hui Z. Novel cooperative power control game algorithm for cognitive radio systems[J]. Journal of Communications, 2007, 28(8): 54-60.)
[7] 胡图, 景志宏, 张秋林. 基于离散功率空间的定步长认知无线网络功率控制算法[J]. 重庆邮电大学学报(自然科学版), 2012, 24(1): 14-19.
(Tu H, ZhihongJ Qiulin Z. Fixed step power control algorithm for cognitive wireless networks based on the discrete power space[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2012, 24(1): 14-19.)
[8] 张龙, 周贤伟, 王建萍, 等. CR系统中基于微分博弈的功率控制算法[J]. 电子与信息学报, 2010, 32(1): 141-145.
(Long Z, Xianwei Z, Jianping W, et al.Power control algorithm based on differential game for CR system[J]. Journal of Electronics and Information Technology, 2010, 32(1): 141-145.)
[9] Zhu Y, Sun J, Shao S, et al.Nash Game-Theoretical Algorithm For Power Control In Cognitive Radio Networks[C]//Communication Technology (ICCT), 2010 12th IEEE International Conference on, 2010: 730-733.
[10] 罗伯特·吉本斯. 博弈论基础 [M]. 张峰译. 北京: 中国社会科学出版社, 1999.
[11] Wang B, Wu Y, Liu K J R. Game theory for cognitive radio networks: An overview[J]. Computer Networks the International Journal of Computer & Telecommunications Networking (S1389-1286), 2010, 54(14): 2537-2561.
[12] Li F, Tan X, Wang L.A New Game Algorithm for Power Control in Cognitive Radio Networks[J]. IEEE Transactions on Vehicular Technology (S0018-9545), 2011, 60(9): 4384-4391.
[13] Wang S, Huang F, Zhou Z H.Fast Power Allocation Algorithm for Cognitive Radio Networks[J]. IEEE Communications Letters (S1089-7798), 2011, 15(8): 845-847.
[14] De S C F, Abbas-Turki M, Abou-KandilH, et al. Transmission Power Control for Opportunistic QoS Provision in Wireless Networks[J]. IEEE Transactions on Control Systems Technology (S1063-6536), 2013, 21(2): 315-331.
[15] Ni Q, Zarakovitis C C.Nash Bargaining Game Theoretic Scheduling for Joint Channel and Power Allocation in Cognitive Radio Systems[J]. IEEE Journal on Selected Areas in Communications (S0733-8716), 2012, 30(1): 70-81.
[16] David Goodman, Narayan Mandayam.Power Control for Wireless Data[J]. IEEE Personal Communications, 2000, 7(2): 48-54.
[17] Foschini G J, Miljanic Z.A simple distributed autonomous power control algorithm and its convergence[J]. IEEE Transactions on Vehicular Technology (S0018-9545), 1993, 42(4): 641-646.
[18] Koskie S.A Nash Game Algorithm for SIR-based Power Control in 3G Wireless CDMA Networks[J]. IEEE/ACM Transactions on Networking (S1063-6692), 2005, 13(5): 1017-1026.
[19] Jiao J, Jiang L, He C.A Novel Game Theoretic Utility Function for Power Control in Cognitive Radio Networks[C]// International Conference on Computational and Information Sciences. DC, USA: IEEE Computer Society, 2013: 1553-1557.
[20] 张峰. 论博弈逻辑的分析方法—纳什均衡分析法[J]. 北京理工大学学报(社会科学版), 2008, 10(2): 95-99.
(Feng Z.On the analysis method of Nash equilibrium in game logic[J]. Journal of Beijing Institute of Technology (Social Sciences Edition), 2008, 10(2): 95-99.)
[21] 云磊. 牛顿迭代法的MATLAB实现[J]. 信息通信, 2011(6): 20-20.
(Yun Lei.Newton iterative method of MATLAB[J]. Information and Communications, 2011 (6): 20-20.)
[22] 周宗福, 蒋威. 隐函数存在定理的新证明[J]. 大学数学, 2007, 23(5): 137-138.
(Zongfu Z, Wei J.New proof of the existence theorem of implicit function[J]. College Mathematics, 2007, 23(5): 137-138.)