计算机科学 ›› 2020, Vol. 47 ›› Issue (12): 273-278.doi: 10.11896/jsjkx.190900199

• 计算机网络 • 上一篇    下一篇

基于改进乌鸦算法的车载网络频谱分配方案

樊英, 张达敏, 陈忠云, 王依柔, 徐航, 王栎桥   

  1. 贵州大学大数据与信息工程学院 贵阳 550025
  • 收稿日期:2019-09-29 修回日期:2020-01-02 发布日期:2020-12-17
  • 通讯作者: 张达敏(1203813362@qq.com)
  • 作者简介:yffx0311@163.com
  • 基金资助:
    贵州省自然科学基金(黔科合基础1047号)

Spectrum Allocation Scheme of Vehicular Ad Hoc Networks Based on Improved Crow Search Algorithm

FAN Ying, ZHANG Da-min, CHEN Zhong-yun, WANG Yi-rou, XU Hang, WANG Li-qiao   

  1. College of Big Data & Information Engineering Guizhou University Guiyang 550025,China
  • Received:2019-09-29 Revised:2020-01-02 Published:2020-12-17
  • About author:FAN Ying,born in 1995master.Her main research interests include internet of vehiclescomputer application Technology and optimization calculation.
    ZHANG Da-min,born in 1967Ph.Dprofessor.His main research interests include computer application Technology intelligent algorithmsignal and information processing.
  • Supported by:
    Natural Science Foundation of Guizhou Province,China ([2017]1047).

摘要: 车载网络(Vehicle Ad Hoc Networks)是一种新型的智能网络它通过智能地接入网络实现人与车、车与车、车与路边基础设施之间的互联通信增强车辆行驶过程中的安全预测报警功能满足用户对车辆多媒体接入的需求提升车辆用户体验.针对认知车载网络(Cognitive Vehicular AdHoc NetworksCR-VANET)频谱分配效率低的问题文中提出一种基于改进乌鸦算法的频谱分配方案.首先对乌鸦算法的两个位置更新参数引用曲线自适应参数进行改进以更好地平衡集约化与多元化;其次采用收敛因子策略解决乌鸦算法收敛速度慢和不稳定的问题;然后对随机数混沌化以提高搜索的遍历性和收敛速度;最后以车载网络吞吐量和认知车载用户之间的接入公平性作为参考评价指标将改进后的乌鸦算法应用于认知车载网络的频谱分配中.实验采用改进的方案、遗传算法(Genetic AlgorithmGA)、粒子群算法(Particle Swarm Optimization AlgorithmPSO)分配方案进行比较.仿真结果表明改进的分配方案具有较好的性能.

关键词: 二进制乌鸦算法, 混沌映射, 频谱分配, 认知车载网络, 收敛因子, 自适应曲线

Abstract: The vehicle Ad Hoc network is a new type of intelligent network.By intelligently accessing the networkit realizes the interconnection communication between people and vehiclesvehicles and vehiclesvehicles and infrastructure of roadsideenhances the safety prediction and alarm during the driving process of the vehiclesatisfies users' needs of vehicle multimedia accessand thus improves vehicle users' experience.Aiming at the problem of low efficiency of spectrum allocation in cognitive vehicular Ad Hoc networks(CR-VANET)a spectrum allocation scheme based on improved crow algorithm is proposed.Firstlythe two updated position parameters of the crow algorithm are improved by referencing curve adaptive parameters to better balance intensification and diversification.Secondlythe convergence factor strategy is adopted to solve the problem of slow convergence and instability of the crow algorithm.Thirdlythe chaotic map is used for random numbers to improve the ergodicity and convergence speed of the search.Finallythe throughput of the vehicle network and the access fairness between the users of cognitive vehicle are used as the reference evaluation indexthe improved crow algorithm is applied to the spectrum allocation of the cognitive vehicle network.The improved schemeis seperately compared with genetic algorithm(GA) and particle swarm optimization algorithm(PSO) allocation scheme.Simulation results show that the improved allocation scheme has a better performance.

Key words: Adaptive curve, Binary crow algorithm, Chaotic map, Convergence factor, CR-VANET, Spectrum allocation

中图分类号: 

  • TP393
[1] THAKKER P,SARKANIS,MAZUCHI T.A system dynamics approach to demand and allocation of wireless spectrum for mobile communication[J].Procedia Computer Science,2012,8:118-123.
[2] MITOLA J I,MAGUIRE G Q.Cognitive radio:making software radios more personal[J].IEEE Personal Communications,1999,6(4):13-18.
[3] DI FELICE M,DOOST-MOHAMMADY R,CHOWDHURY K R,et al.Smart radios forsmart vehicles:cognitive vehicular networks[J].IEEE Vehicular Technology Magazine,2012,7(2):26-33.
[4] ZAYEN B,HAYAR A,NOUBIR G.Game theory based re-source management strategy for cognitive radio networks[J].Multimedia Tools and Applications,2014,70(3):2063-2083.
[5] LI Z,LI B,ZHU Y.Designing truthful spectrum auctions for multi-hop secondary networks[J].IEEE Transactions on Mobile Computing,2015,14(2):316-327.
[6] PENG C Y,ZHENG H T,ZHAO B Y.Utilization and fairness in spectrum assignment foropportuneistic spectrum access[J].Mobile Networks and Applications,2006,11(4):555-576.
[7] CAI C,WANG Y F,MIAO B M,et al.Dynamic spectrum allocation for cognitive radio sensor networks based on improved genetic algorithm[J].Telecommunications Science,2017,33(8):85-93.
[8] HONG B D,DONG J L I,XIAO P Z.Particle Swarm Optimization Algorithm with Dynamically Adjusting Inertia Weight[J].Computer Science,2018,45(2):98-102,13.
[9] XUAN W U,WEN S S.Cognitive radio spectrum allocationbased on genetic ant colony optimization[J].Communications Technology ,2015,48(11):1265-1269.
[10] GAO H Y,CAO J L.Quantum-inspired bee colony optimization algorithm and its application for cognitive radio spectrum allocation[J].Journal of Central South University,2012,43(12):4743-4749.
[11] WANG X P,CAO H.Spectrum allocation based on quantum cuckoo search algorithm in cognitive radio network[J].Telecommunications Science,2016,32(5):62-68.
[12] JIANG T,WANG Z Q,ZHANG L,et al.Efficient spectrum utilization on TV band for cognitive radio based high speed vehicle network[J].IEEE Transactions on Wireless Communications,2014,13(10):5319-5329.
[13] CHENG N,ZHANG N,LU N,et al.Opportunistic Spectrum Access for CR-VANETs:A Game-Theoretic Approach[J].IEEE Transactions on Vehicular Technology ,2014,63(1):237-251.
[14] GUPTA P,KUMAR P R.The capacity of wireless networks[J].IEEE Transactions on Information Theory,2000,46(2):388-404.
[15] ABDELAZIZ A Y,FATHY A.A novel approach based on crow search algorithm for optimal selection of conductor size in radial distribution networks[J].Engineering Science &Technology an International Journal,2017,20(2):391-402.
[16] LIU X J,LU F J,WU C C,et al.Differential crow algorithmbased on Levy flight to solve discount {0-1} knapsack problem[J].Computer Application,2018,10(71):1161-1175.
[17] HAUPT R L,HAUPT S E.Practical genetic algorithms[M].Berlin,Heidelberg:Springer,2006.
[18] KALLAH R M,HASSANIEN A E.Chaotic crow search algorithm for fractional optimization problems[J].Applied Soft Computing,2018,10(71):1161-1175.
[19] ZHANG D M,CHEN Z Y,XIN Z Y,et al.Salp swarm algorithm based on craziness and adaptive[J/OL].Control and Decision.[2019-09-229].https://doi.org/10.13-195/j.kzyjc.2019.0012.
[20] CHEN Z,QIUR C.Q-learning based bidding algorithm for spectrum auction in cognitive radio[C]//Proceedings of IEEE Southeastcon.2011:409-412.
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