双信任度加权的K秩准则频谱感知算法

基于信任度的协作频谱感知算法,可以很好地解决本地检测的隐藏终端和频谱衰落等问题。但其对于多用户的大量数据分析,缺乏高效的数据统计和融合算法,算法的检测性能有待提高。为此,提出了一种基于双信任度加权的K秩准则协作频谱感知算法。它在主用户存在与不存在两种情况下,分别采用不同的信任度加权的算法,并与K秩准则的融合策略相结合。仿真结果表明,算法在保证数据统计和融合能力的前提下,有效的提高了检测性能和感知能力。

K-rank Criteria Spectrum Sensing Algorithm based on Dual-Trust-Weight

Trust-based cooperative spectrum sensing algorithm could solve the locally detected hidden terminal,spectrum decline and some other problems.However,for large amount of multi-user data analysis and the lack of efficient data statistics and fusion algorithm,it is necessary to improve detection performance of the algorithm.For this purpose,a K-rank criterion cooperative spectrum sensing algorithm based on dual-trust-weight is proposed.For the situations that the primary users exist or not,the algorithm with different trust weights are adopted respectively,including the combination of fusion strategy of the K-rank criteria.Simulation results show that this proposed algorithm,under the premise of ensuring statistics and fusion capabilities,could effectively improve the perception capability and detection performance.