In this paper, we study the problem of sparse channel estimation via a collaborative and fully distributed approach. The estimation problem is formulated in the angular domain by exploiting the spatially common sparsity structure of the involved channels in a multi-user scenario. The sparse channel estimation problem is solved via an efficient distributed approach in which the participating users collaboratively estimate their channel sparsity support sets, before locally estimate the channel values, under the assumption that global and common support subsets are present. The performance of the proposed algorithm, named WDiOMP, is compared to DiOMP, local OMP and a centralized solution based on SOMP, in terms of the support set recovery error under various experimental scenarios. The efficacy of WDiOMP is demonstrated even in the case in which the underlining sparsity structure is unknown.
翻译:在本文中,我们通过合作和完全分布的方法研究频道估计稀少的问题;在多用户情况下,通过利用所涉渠道空间上共同的宽度结构,在角域提出估算问题;在多种用户情况下,通过高效分布方法解决频道估计稀少的问题,参与用户在对频道宽度支持组进行当地估计之前,根据全球和共同支持子集的假设,在当地估计频道值之前,在当地估计频道的宽度支持组;拟议的算法(名为WDiOMP)的性能与DiOMP、当地OMP和基于SOMP的集中解决方案相比较,在各种实验情景下,从支助设定的回收错误来看,WDiOMP的功效甚至表现在支撑宽度结构未知的情况下。