指数加权协作频谱感知算法分析及应用

Performance Analysis and Application of Exponential Weighted Collaborative Spectrum Sensing Scheme

为进一步提高认知无线电网络协作频谱感知性能,提出了一种新的加权软合并算法,该算法以信噪比指数形式进行权值计算,其目标是使权值-信噪比曲线更好地逼近检测概率-信噪比曲线,从而更好地利用信噪比信息来提高网络的协作感知性能。在单用户感知的基础上,介绍了指数加权软合并算法的思想和模型;其后以能量感知为基础,分别在Rayleigh和Nakagami衰落信道下,将指数加权软合并算法与传统软合并算法进行仿真比较,并分析得到了该算法的最优门限值;最后将新算法应用到基于簇的协作频谱感知中并进行仿真分析。仿真结果表明,新算法具有明显的性能提升。

This paper introduces a new weighted soft combination algorithm in order to further enhance the performance of collaborative spectrum sensing in cognitive radio network.The algorithm calculates weights by exponential form of signal to noise ratio,with the goal to reach better approximation of weights-SNR curve to probability of detection-SNR curve so as to better use SNR information to improve the performance of collaborative spectrum sensing.The idea and model of exponential weighted soft combination are introduced based on the sensing model.Based on energy detection,the paper simulates and compares the performances of this new algorithm and traditional soft combination algorithms in Rayleigh and Nakagami fading channel.Then the optimal threshold is obtained by analyzing of the algorithm.Finally,the new algorithm is applied to cluster-based collaborative spectrum sensing and is simulated for performance analysis.The results indicate that the proposed algorithm can effectively improve detection performance.