基于改进蚁群算法的认知无线电频谱分配的策略研究  

Cognitive Radio Spectrum Allocation Strategy based on Modified Ant Colony Algorithm

作　　者：赵显煜 王俊[1] 邢新华 ZHAO Xian-yu;WANG Jun;XING Xin-hua(Institute of Electrical and Automation Engineering,Fuzhou University,Fujian Fuzhou 350108,China)

机构地区：[1]福州大学电气工程与自动化学院,福建福州350108

出　　处：《通信技术》2020年第10期2454-2460,共7页Communications Technology

基　　金：国家自然科学基金“基于能量采集的认知无线电频谱感知及接入方法的研究”(No.61871133)。

摘　　要：在认知无线电网络中,为了有效使用空闲频谱资源,提升频谱利用率,提出了一种基于图论模型的改进蚁群算法。该方法在信息素更新时引入了混沌初始化和混沌扰动,改变了传统蚁群算法信息素更新单一的规则,提升了算法的寻优能力,进而加快了收敛速度,同时在传统模型基础上考虑了认知用户选择信道的优先级,重新设定了干扰约束条件,增加了认知用户对信道的需求大小问题,进而改善了认知用户的公平性。将改进的蚁群算法(Improved Ant Cology Algorithm,IACA)应用于认知无线电频谱分配问题,结果表明,基于IACA的认知无线网络频谱分配方法具有更快的收敛速度和更好的寻优性能,能有效提高系统的网络效益。In cognitive radio networks,in order to effectively use idle spectrum resources and improve spectrum utilization,a modified ant colony algorithm based on graph theory model is proposed.This method introduces chaotic initialization and chaotic disturbance during pheromone update,which changes the single rule of traditional ant colony algorithm,for pheromone update,improves the optimization ability of the algorithm,and accelerates the convergence speed.Meanwhile,based on the traditional model,the priority of cognitive users in selecting channel is considered,the interference constraints are reset so as to increase the demand of cognitive users for the channel and improve the fairness of cognitive users.The modified ant colony algorithm(IACA)is applied to the spectrum allocation problem of cognitive radio.The simulation results indicate that the spectrum allocation method based on IACA has faster convergence speed and better optimization performance,and can effectively improve the network benefits of the system.

关 键 词：认知无线电 频谱分配 改进蚁群算法 混沌系统 图论着色 

分 类 号：TN925[电子电信—通信与信息系统]