基于混沌和声搜索的最优线性协作频谱感知算法

为了进一步提高认知无线电频谱感知的准确性和可靠性,提出一种基于混沌和声搜索(CHS)的最优线性协作频谱感知算法。该算法基于能量检测的线性加权协作频谱感知模型,利用和声搜索(HS)算法本身的优化能力和混沌映射的遍历性、随机性等特点,通过求解最优权值的方法,提高频谱感知的性能。仿真实验结果表明,CHS算法的优化性能和收敛速度均优于传统的HS算法,基于CHS的最优线性协作频谱感知算法能够找到最优的权值,从而提高了复杂通信环境下的检测概率;并且在相同的虚警概率下,所提算法性能优于基于修正偏差因子的协作频谱感知算法。

Optimal linear cooperation spectrum sensing method based on chaos harmony search algorithm

In order to improve the accuracy and reliability of cognitive radio spectrum sensing,an optimal linear cooperation spectrum sensing method based on Chaos Harmony Search(CHS) algorithm was proposed in this paper.This algorithm is based on the linear weighted cooperative spectrum sensing model with energy detection,using the optimization capability of Harmony Search(HS) and the traverse and randomness of chaotic system to find the optimal weight values and then improve the performances of spectrum sensing for cognitive radio networks.The simulation results show that the proposed algorithm has better optimal performance and convergence speed than the traditional HS algorithm.This CHS algorithm can obtain optimal weight values and improve the probability of detection in complex communications environment.Besides,cooperation spectrum sensing performance based on the proposed algorithm is better than the existing Modified Deflection Coefficient(MDC) method with the same false probability.