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 Ad Hoc Networks,CR-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 Algorithm,GA)、粒子群算法(Particle Swarm Optimization Algorithm,PSO)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。
The vehicle Ad Hoc network is a new type of intelligent network.By intelligently accessing the network,it realizes the interconnection communication between people and vehicles,vehicles and vehicles,vehicles and infrastructure of roadside,enhances the safety prediction and alarm during the driving process of the vehicle,satisfies users’needs of vehicle multimedia access,and 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.Firstly,the two updated position parameters of the crow algorithm are improved by referencing curve adaptive parameters to better balance intensification and diversification.Secondly,the convergence factor strategy is adopted to solve the problem of slow convergence and instability of the crow algorithm.Thirdly,the chaotic map is used for random numbers to improve the ergodicity and convergence speed of the search.Finally,the throughput of the vehicle network and the access fairness between the users of cognitive vehicle are used as the reference evaluation index,the improved crow algorithm is applied to the spectrum allocation of the cognitive vehicle network.The improved scheme is 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.
作者
樊英
张达敏
陈忠云
王依柔
徐航
王栎桥
FAN Ying;ZHANG Da-min;CHEN Zhong-yun;WANG Yi-rou;XU Hang;WANG Li-qiao(College of Big Data&Information Engineering,Guizhou University,Guiyang 550025,China)
出处
《计算机科学》
CSCD
北大核心
2020年第12期273-278,共6页
Computer Science
基金
贵州省自然科学基金(黔科合基础[2017]1047号)。