Low-complexity near-optimal signal detection for uplink large-scale MIMO systems

Minimum mean square error (MMSE) signal detection algorithm is near- optimal for uplink multi-user large-scale multiple input multiple output (MIMO) systems, but involves matrix inversion with high complexity. In this letter, we firstly prove that the MMSE filtering matrix for large- scale MIMO is s...

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Bibliographic Details
Main Authors Gao, Xinyu, Dai, Linglong, Ma, Yongkui, Wang, Zhaocheng
Format Journal Article
LanguageEnglish
Published 15.07.2015
Subjects
Computer Science - Information Theory
Mathematics - Information Theory
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Summary:Minimum mean square error (MMSE) signal detection algorithm is near- optimal for uplink multi-user large-scale multiple input multiple output (MIMO) systems, but involves matrix inversion with high complexity. In this letter, we firstly prove that the MMSE filtering matrix for large- scale MIMO is symmetric positive definite, based on which we propose a low-complexity near-optimal signal detection algorithm by exploiting the Richardson method to avoid the matrix inversion. The complexity can be reduced from O(K3) to O(K2), where K is the number of users. We also provide the convergence proof of the proposed algorithm. Simulation results show that the proposed signal detection algorithm converges fast, and achieves the near-optimal performance of the classical MMSE algorithm.
DOI:10.48550/arxiv.1507.04443