Gamma‐distribution‐based logit weighted block orthogonal matching pursuit for compressed sensing

Abstract

Block orthogonal matching pursuit is an efficient reconstruction algorithm in compressed sensing, which exploits block sparsity during support index selection. In this letter, to further improve the performance, the authors propose two block sparse reconstruction algorithms by incorporating the prior information of block support probability. Based on Gamma distribution approximation, such information is formulated as an additive term during index selection. Moreover, the second algorithm extends the first one to the scenario with inaccurate prior information by introducing an additional judging mechanism with block correlation and prior factor simultaneously. Numerical results show that the proposed algorithms outperform existing algorithms.

Downlink Massive MIMO Channel Estimation via Deep Unrolling : Sparsity Exploitations in Angular Domain

“…To capture the intra-frame sparsity and small-scale interframe sparsity, we aim to refine the coarsely estimated channel in a frame-wise way and exploit the small-scale inter-frame sparsity in the form of partial support information inspired by [11], [29]- [31]. The following weighted 2,1 minimization problem of the fine correction part is defined as follows:…”