摘要： 在认知无线电网络中，单一子频段检测信息融合效率低，且融合过程中权值系数为固定值不能实现最优化。为解决该问题，提出一种重加权分布式多目标决策融合算法。该算法并行检测多个子频段，将自适应的稀疏权值矩阵运用在分布式决策融合算法中，利用最速下降法对优化问题进行求解，并结合用户与信道信息选取最佳的合作用户及数量。实验结果表明，该算法在低信噪比环境下的检测概率和稳定性能都得到较大提高。

关键词: 认知无线电, 协同频谱检测, 平均一致性, 决策融合, 压缩感知, 最速下降法

Abstract: A novel decision fusion algorithm of reweighted distributed multi-objects is proposed for poor efficiency of information fusion by single sub-band in Cognitive Radio(CR), and the situation cannot realize optimization with fixed weight during the fusion process. The algorithm detects multiple sub-bands at the same time, using adaptive sparse weight matrix to solve the algorithm, and solves the optimization problem by steepest descent method, and combines the information of CR and channels to choose the optimal collaborative users. Experimental results show that the algorithm raises the detective probability and stability in the low SNR condition.

Key words: Cognitive Radio(CR), collaborative spectrum detection, average consensus, decision fusion, compressive sensing, steepest descent method

中图分类号: 

• TP391