一种稀疏度自适应的压缩感知信道估计算法  

A sparsity adaptive CS-based channel estimation algorithm

作　　者：林思铭[1,2] 彭卫东[2] 李明阳[3] 林志国[1,2] 李瑞[1,2] 

机构地区：[1]空军工程大学装备管理与安全工程学院 [2]空军工程大学装备发展与运用研究中心 [3]中国人民解放军95972部队

出　　处：《合肥工业大学学报（自然科学版）》2016年第8期1055-1059,共5页Journal of Hefei University of Technology：Natural Science

基　　金：国家自然科学基金资助项目(6150051163)

摘　　要：针对目前稀疏度自适应的压缩感知(compressed sensing,CS)信道估计算法计算量过大的问题,文章提出了基于关联度分析的稀疏度自适应归档正则化迭代硬阈值(sparsity adaptive archiving normalized iterative hard thresholding,SAANIHT)算法。ANIHT算法可以解决传统压缩感知理论计算量大、计算时间过长的问题,但需要预知信道的稀疏度。引入高斯核函数对一种稀疏度估计算法进行了改进,并与ANIHT算法结合,使其可以在盲稀疏情况下对信道进行估计。仿真结果表明,在同等稀疏度条件下,该算法比其他算法节约了计算时间,在低信噪比下性能更优,具有较好的重构性能与稳定性。The traditional sparsity adaptive multipath channel estimation algorithms based on com- pressed sensing（CS） cost too much time. For solving this problem, a sparsity adaptive archiving nor- malized iterative hard thresholding（SAANIHT） algorithm based on correlation analysis is proposed. The ANIHT algorithm has fewer calculation amount than traditional algorithm only if the channel sparsity degree has been known. In order to make the algorithm possess the ability of blind sparse channel estimation, a sparsity adaptive method optimized by the Gaussian kernel function is intro- duced. The simulation results show that for the same sparsity degree, the proposed algorithm costs fewer time than other algorithms and has better performance in low signal-to-noise ratio and better convergence performance and stability.

关 键 词：压缩感知 稀疏多径信道估计 归档正则化迭代硬阀值算法 高斯核函数 稀疏度自适应 

分 类 号：TN911.5[电子电信—通信与信息系统]