Double-Sparsity Learning Based Channel-and-Signal Estimation in Massive MIMO with Generalized Spatial Modulation
Xiaoyan Kuai, Xiaojun Yuan, Wenjing Yan, Hang Liu, and Ying Jun, (Angela) Zhang

TL;DR
This paper introduces a double-sparsity model for massive MIMO with GSM and develops novel message-passing algorithms for joint channel and signal estimation, significantly improving performance over existing methods.
Contribution
The paper proposes a double-sparsity model and develops BCSE and SBCSE algorithms for blind and semi-blind estimation in massive MIMO with GSM, addressing phase and permutation ambiguities.
Findings
BCSE algorithm outperforms existing blind and training-based algorithms.
SBCSE algorithm significantly improves estimation with short pilots.
Proposed methods approach genie bounds in performance.
Abstract
In this paper, we study joint antenna activity detection, channel estimation, and multiuser detection for massive multiple-input multiple-output (MIMO) systems with general spatial modulation (GSM). We first establish a double-sparsity massive MIMO model by considering the channel sparsity of the massive MIMO channel and the signal sparsity of GSM. Based on the double-sparsity model, we formulate a blind detection problem. To solve the blind detection problem, we develop message-passing based blind channel-and-signal estimation (BCSE) algorithm. The BCSE algorithm basically follows the affine sparse matrix factorization technique, but with critical modifications to handle the double-sparsity property of the model. We show that the BCSE algorithm significantly outperforms the existing blind and training-based algorithms, and is able to closely approach the genie bounds (with either known…
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Taxonomy
TopicsSparse and Compressive Sensing Techniques · Advanced MIMO Systems Optimization · Full-Duplex Wireless Communications
