论文部分内容阅读
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。
The cooperative spectrum sensing algorithm based on trust can well solve the problems of locally detected hidden terminals and spectrum fading. However, its large data analysis for multi-user, the lack of efficient data statistics and fusion algorithms, the algorithm’s detection performance needs to be improved. To this end, a K-rank criterion cooperative spectrum sensing algorithm based on dual-trust weighting is proposed. It uses different confidence weighted algorithms in the presence and absence of the primary user, and combines with the fusion strategy of K-rank criterion. The simulation results show that the algorithm can effectively improve the detection performance and perception under the premise of ensuring the statistical and fusion capabilities of the data.