摘要：

为提高双选择性衰落环境下的宽带频谱感知性能,提出一种快速低复杂度线性Bregman 算法。利用该算法可实现基于循环谱估计的宽带压缩频谱检测。针对一般线性Bregman 算法存在冗余迭代计算的缺陷,通过增加辅助变量,估计线性Bregman 算法中余量保持不变过程的迭代次数,更新辅助变量值,跳出冗余迭代的过程,从而加速算法的收敛速度,同时降低算法复杂度。实验结果表明,与一般线性Bregman 算法相比,该算法在双选衰落环境下的压缩采样重构效果、检测概率和收敛速度性能均有所提高。

关键词: 认知无线电, 宽带频谱感知, 压缩采样, 线性Bregman 算法, 双选择性衰落信道, 恒虚警率

Abstract:

This paper presents a kind of fast and low complexity linear Bregman algorithm and lists the specific implementation steps of the algorithm in order to improve the performance of the wideband spectrum sensing in doubly selective fading environment,which is used for the process of testing wideband compressed spectrum based on cyclic spectrum estimation. This algorithm aims at the flaw of redundant iterative computation in general linear Bregman algorithm. It can accelerate the convergence speed by adding auxiliary variables to estimate iterations of residual staying almost constant in linear Bregman algorithm and updating auxiliary variables to step out the process of redundant iteration. At the same time,it reduces the complexity of the algorithm. As a result,compared with general linear Bregman algorithm,the effect of compressive sampling reconstruction,the detection probability and convergence speed of the modified algorithm are improved in doubly selective fading environment.

Key words: Cognitive Radio (CR), wideband spectrum sensing, compressive sampling, linear Bregman algorithm, doubly-selective fading channel, Constant False Alarm Rate(CFAR)

中图分类号: 

• TP393