计算机科学 ›› 2013, Vol. 40 ›› Issue (8): 49-52.

• 网络与通信 • 上一篇    下一篇

改进的细菌觅食算法求解认知无线网络频谱分配问题

李岳洪,万频,王永华,邓钦,杨健   

  1. 广东工业大学自动化学院 广州510006;广东工业大学自动化学院 广州510006;广东工业大学自动化学院 广州510006;广东工业大学自动化学院 广州510006;广东工业大学自动化学院 广州510006
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受国家自然科学基金(61172156,61102034),深圳市生物、互联网、新能源产业发展专项资金(CXB201005250021A),广东工业大学博士启动基金(103042),广东工业大学团队平台重大成果培育基金项目(GDUT2011-10)资助

Cognitive Radio Spectrum Assignment Based on Binary Bacterial Foraging Optimization Algorithm

LI Yue-hong,WAN Pin,WANG Yong-hua,DENG Qin and YANG Jian   

  • Online:2018-11-16 Published:2018-11-16

摘要: 认知无线网络中如何进行频谱合理的分配是实现动态频谱接入的关键技术之一。基于图论着色频谱分配模型,以最大化网络效益为目标函数,提出一种具有量子变异操作的改进的二进制细菌觅食优化算法,用以求解认知无线网络中空闲频谱在认知用户间的动态分配问题。通过仿真实验比较了本算法与颜色敏感图论着色算法、传统二进制细菌觅食算法的性能。结果表明:本算法性能明显优于颜色敏感图论着色算法,能更好地实现网络效益最大化,提高用户的平均效益;与传统二进制细菌觅食算法相比,改进后的细菌觅食算法寻优能力更强,收敛速度更快。

关键词: 认知无线网络,频谱分配,细菌觅食算法,图论着色,量子变异

Abstract: How to make efficient spectrum allocation of cognitive wireless network is the key technology for dynamic spectrum access.This paper presented an improved binary bacterial foraging optimization algorithm with quantum variation operation based on the graph coloring theory model of spectrum assignment,and used the maximum system efficiency of cognitive wireless network as the objective function,achieving the free radio frequency spectrum’s dynamic allocation among the cognitive users.Simulations were conducted to compare this algorithm with color sensitive graph co-loring algorithm and traditional binary bacterial foraging optimization algorithm.Results show that the proposed algorithm has better performances.It can achieve the maximization of network utility and increase the second user’s average utility.Compared with the traditional binary bacterial foraging optimization algorithm,it has better optimization ability and faster convergence speed.

Key words: Cognitive wireless network,Spectrum assignment,Bacterial foraging optimization algorithm,Graph coloring,Quantum variation operation

[1] Mitola J.Cognitive radio:making software radios more personal[J].IEEE Personal Communication,1999,6(4)
[2] Haykin S.Cognitive radio:brain-empowered wireless communications [J].IEEE Journal on Selected Areas in Communications,2005,23(2):201-220
[3] Zheng H,Cao L.Device-centdc apectrum management [C]∥IEEE DySPAN.2005,11:56-65
[4] Kloeck C,Jaekel H,Jondral F K.Dynamic and local combined pricing,allocation and billing system with cognitive radios [C]∥The First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks(DySPAN).2005:73-81
[5] Zheng H,Peng C.Collaboration and fairness in opportullisticspectrum access[C]∥Proc.40th annual IEEE International Conference on Communications(ICC).seoul,Korea,2005:3132-3136
[6] Peng Chun-yi,Zheng Hai-tao,Zhao B Y.Utilization and Fairness in Spectrum Assignment for Opportunistinc Spectrum Access [J].ACM Mobile Networks and Applications,2006,11(4):555-576
[7] 廖楚林,陈劼,唐友喜,等.认知无线电中的并行频谱分配算法[J].电子与信息学报,2007,29(7):1609-1611
[8] 赵知劲,彭振,郑仕链,等.基于量子遗传算法的认知无线电频谱分配[J].物理学报,2009,58(2):1358-1363
[9] 柴争义,刘芳,朱思峰.混沌量子克隆算法求解认知无线网络频谱分配问题[J].物理学报,2011,0(6):828-835
[10] Passino K M.Biomimicry of bacterial foraging for distributedoptimization and control[J].IEEE Control Systems Magazine,2002,22(3):52-67
[11] Das S,Biswas A,Dasgupta S,et al.Bacterial foraging optimization algorithm:Theoretical foundations,analysis,and applications[J].Foundations of Comput Intel,2009,3(203):23-55
[12] Liu Y,Passino K M.Biomimicry of social foraging bacteria for distributed optimization:Models,principles,and emergent behav-iors [J].Optimization Theory and Applications,2002,1(3):603-628
[13] 杨淑媛,焦李成,刘芳.量子进化算法[J].工程数学学报,2006,23(2):235-246

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