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Fast Converging Gauss-Seidel Iterative Algorithm for Massive MIMO Systems
Version 1
: Received: 13 October 2023 / Approved: 13 October 2023 / Online: 16 October 2023 (08:38:10 CEST)
A peer-reviewed article of this Preprint also exists.
Shen, D.; Chen, L.; Liang, H. Fast Converging Gauss–Seidel Iterative Algorithm for Massive MIMO Systems. Appl. Sci. 2023, 13, 12638. Shen, D.; Chen, L.; Liang, H. Fast Converging Gauss–Seidel Iterative Algorithm for Massive MIMO Systems. Appl. Sci. 2023, 13, 12638.
Abstract
For massive multiple-input multiple-output (MIMO) communications, the minimum mean square error (MMSE) scheme provides close to optimal recognition but involves inverting the high-dimensional matrix. A Gauss-Seidel (GS) detector based on conjugate gradient and Jacobi iteration (CJ) joint processing is introduced to address this problem. Firstly, the signal is initialized with the best of the three initialization regimes for faster algorithm convergence. Secondly, the signal is processed together with CJ. Finally, the pre-processed result is transferred to the GS detector. The simulation results indicate that the proposed iterative algorithm has a lower BER performance than the GS and improved iterative scheme based on GS in channels with different correlation levels.
Keywords
Massive MIMO; Conjugate Gradient; Jacobi; Gauss-Seidel; Kronecker Channel
Subject
Computer Science and Mathematics, Signal Processing
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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