Error Recovery for Massive MIMO Signal Detection via Reconstruction of Discrete-Valued Sparse Vector

Ryo HAYAKAWA  Kazunori HAYASHI  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E100-A   No.12   pp.2671-2679
Publication Date: 2017/12/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E100.A.2671
Type of Manuscript: Special Section PAPER (Special Section on Information Theory and Its Applications)
Category: Communication Theory and Systems
Keyword: 
massive MIMO,  signal detection,  sum-of-absolute-value optimization,  restricted isometry property,  

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Summary: 
In this paper, we propose a novel error recovery method for massive multiple-input multiple-output (MIMO) signal detection, which improves an estimate of transmitted signals by taking advantage of the sparsity and the discreteness of the error signal. We firstly formulate the error recovery problem as the maximum a posteriori (MAP) estimation and then relax the MAP estimation into a convex optimization problem, which reconstructs a discrete-valued sparse vector from its linear measurements. By using the restricted isometry property (RIP), we also provide a theoretical upper bound of the size of the reconstruction error with the optimization problem. Simulation results show that the proposed error recovery method has better bit error rate (BER) performance than that of the conventional error recovery method.