Matrix Decomposition for Massive MIMO Detection

Shahabuddin, Shahriar; Islam, Muhammad Hasibul; Shahabuddin, Mohammad Shahanewaz; Albreem, Mahmoud A.; Juntti, Markku

doi:10.48550/arxiv.2009.11172

Cited by 1 publication

(1 citation statement)

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“…A substantial literature in detection schemes for massive MIMO UL has appeared and comprehensive surveys are presented in [18,20]. In [21], the computational complexity of linear detection mechanisms based on the QR, Cholesky, and LDL decomposition algorithms for different massive MIMO configurations is presented. Other detectors, such as the sphere decoding (SD), require the QR-decomposition which increases the computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Data Detection Techniques for 5G Massive MIMO Systems

et al. 2020

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Massive multiple-input multiple-output (MIMO) is a backbone technology in the fifth-generation (5G) and beyond 5G (B5G) networks. It enhances performance gain, energy efficiency, and spectral efficiency. Unfortunately, a massive number of antennas need sophisticated processing to detect the transmitted signal. Although a detector based on the maximum likelihood (ML) is optimal, it incurs a high computational complexity, and hence, it is not hardware-friendly. In addition, the conventional linear detectors, such as the minimum mean square error (MMSE), include a matrix inversion, which causes a high computational complexity. As an alternative solution, approximate message passing (AMP) algorithm is proposed for data detection in massive MIMO uplink (UL) detectors. Although the AMP algorithm is converging extremely fast, the convergence is not guaranteed. A good initialization influences the convergence rate and affects the performance substantially together and the complexity. In this paper, we exploit several free-matrix-inversion methods, namely, the successive over-relaxation (SOR), the Gauss–Seidel (GS), and the Jacobi (JA), to initialize the AMP-based massive MIMO UL detector. In other words, hybrid detectors are proposed based on AMP, JA, SOR, and GS with an efficient initialization. Numerical results show that proposed detectors achieve a significant performance enhancement and a large reduction in the computational complexity.

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Section: Introductionmentioning

confidence: 99%