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Large-Scale MIMO Receiver Based on Finite-Alphabet Sparse Detection and Concave-Convex Optimization

Yacine Meslem 1 Abdeldjalil Aïssa-El-Bey 2, 3 Mustapha Djeddou 1
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : In this paper, we propose a new receiver for detecting signals in large-scale Spatially Multiplexed (SP) Multiple-Input-Multiple-Output (MIMO) systems that may have fewer receive antennas than transmitted symbols (overloaded case). Relying on the idea of Finite-Alphabet Sparse (FAS) detection, we formulate the Maximum Likelihood (ML) criterion as a Difference-of-Convex (DC) programming problem that can be simply and efficiently solved using the Concave-Convex Procedure (CCP) technique. Since, the considered problem is non-convex, we theoretically discuss the behavior of the derived algorithm. Numerical experiments confirm the superiority of the proposed detection scheme, when compared with recent detection methods based on convex optimization, in a variety of large-scale MIMO transmission scenarios including the overloaded case.
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Submitted on : Tuesday, June 9, 2020 - 11:24:09 AM
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Yacine Meslem, Abdeldjalil Aïssa-El-Bey, Mustapha Djeddou. Large-Scale MIMO Receiver Based on Finite-Alphabet Sparse Detection and Concave-Convex Optimization. IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), May 2020, Atlanta, United States. ⟨10.1109/SPAWC48557.2020.9154216⟩. ⟨hal-02861823⟩



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