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Reweighted lp constraint LMS-based adaptive sparse channel estimation for cooperative communication system
IET Communications ( IF 1.6 ) Pub Date : 2020-05-14 , DOI: 10.1049/iet-com.2018.6186
Aihua Zhang 1 , Pengcheng Liu 2, 3 , Bing Ning 1 , Qiyu Zhou 1
Affiliation  

The issue of sparsity adaptive channel reconstruction in time-varying cooperative communication networks through the amplify-and-forward transmission scheme is studied. A new sparsity adaptive system identification method is proposed, namely reweighted norm ( ) penalised least mean square (LMS) algorithm. The main idea of the algorithm is to add a norm penalty of sparsity into the cost function of the LMS algorithm. By doing so, the weight factor becomes a balance parameter of the associated norm adaptive sparse system identification. Subsequently, the steady state of the coefficient misalignment vector is derived theoretically, with a performance upper bounds provided which serve as a sufficient condition for the LMS channel estimation of the precise reweighted norm. With the upper bounds, the authors prove that the ( ) norm sparsity inducing cost function is superior to the reweighted norm. An optimal selection of p for the norm problem is studied to recover various d sparse channel vectors. Several experiments verify that the simulation results agree well with the theoretical analysis, and thus demonstrate that the proposed algorithm has a better convergence speed and better steady-state behaviour than other LMS algorithms.

中文翻译:

重新加权 p 约束LMS的协作通信系统自适应稀疏信道估计。

研究了时变协作通信网络中基于放大转发传输方案的稀疏自适应信道重构问题。提出了一种新的稀疏自适应系统识别方法,即重新加权 规范( )的惩罚最小均方(LMS)算法。该算法的主要思想是添加一个将稀疏性的标准惩罚纳入LMS算法的成本函数。这样,权重因子就成为关联的平衡参数规范自适应稀疏系统识别。随后,从理论上推导出系数未对准向量的稳态,并提供性能上限,该上限是精确重新加权的LMS信道估计的充分条件规范。通过上限,作者证明了 )规范稀疏性诱导成本函数优于重新加权 规范。的最佳选择p 为了 研究规范问题以恢复各种 d稀疏通道向量。多次实验验证了仿真结果与理论分析吻合良好,从而表明该算法比其他LMS算法具有更好的收敛速度和更好的稳态行为。
更新日期:2020-05-14
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