基于混合重引力搜索的认知无线电网络协作通信算法

为了提高认知无线电网络的频谱利用效率,提出一种基于混合重引力搜索的认知无线电协作通信算法。将主用户队列平均时延、主用户队列稳定性、次用户平均发送能耗考虑为约束条件,建模次用户数据速率关于传输带宽与传输时长为最大化优化问题。将重引力搜索算法与遗传算法融合,实现高性能的优化算法,对认知无线电协作通信问题进行优化。在重引力搜索的每次迭代中,对每个粒子进行遗传算子的处理,保留重引力搜索健壮性高以及易于实现的优点,同时增强全局搜索能力。仿真实验的结果显示,该算法提高了认知无线电的频带利用率,并且提高了次用户数据传输的连续性与稳定性。

COOPERATION COMMUNICATION ALGORITHM IN COGNITIVE WIRELESS NETWORKS BASED ON HYBRID GRAVITATIONAL SEARCH

To improve the spectrum utilization efficiency of cognitive wireless networks,this paper proposed a cooperation communication algorithm based on hybrid gravitational search algorithm in cognitive wireless networks.The average delay and stability of primary user queue,and the average transmit energy consumption of secondary user were all considered as constraint conditions,the secondary user data rate with respect to transmit bandwidth and transmit duration was modeled as a maximization optimization problem.The genetic algorithm and gravitational search algorithm were combined to realize a high performance optimization algorithm,and the optimization algorithm was used to compute the optimal solution of the cooperation communication problem of cognitive wireless networks.In each iteration of the gravitational search,genetic operators were applied to each particle.The advantages of robustness and simplicity of the gravitational search algorithm was retained,and the global searching ability was enhanced.Simulation experimental results show that the proposed algorithm improves the spectrum utilization efficiency of cognitive wireless networks,and enhances the continuity and stability of secondary user data transmission.