A New Randomized Iterative Detection Algorithm for Uplink Large-Scale MIMO Systems

Wang, Zheng; Xu, Wei; Xia, Yili; Shi, Qingjiang; Huang, Yongming

doi:10.1109/tcomm.2023.3267551

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…In massive MIMO communications, computational cost is a critical metric for signaldetection algorithms [30]. The sophistication of the CJGS iterative scheme consists of three parts, initialization, conjugate gradient and Jacobi co-processing, and GS iteration estimation, and only the number of true multiplicative operations of the method is considered in the analysis.…”

Section: Complexity Analysismentioning

confidence: 99%

Fast Converging Gauss–Seidel Iterative Algorithm for Massive MIMO Systems

Shen,

Chen,

Liang

2023

Applied Sciences

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Signal detection in massive MIMO systems faces many challenges. The minimum mean square error (MMSE) approach for massive multiple-input multiple-output (MIMO) communications offer near to optimal recognition but require inverting the high-dimensional matrix. To tackle this issue, a Gauss–Seidel (GS) detector based on conjugate gradient and Jacobi iteration (CJ) joint processing (CJGS) is presented. In order to accelerate algorithm convergence, the signal is first initialized using the optimal initialization regime among the three options. Second, the signal is processed via the CJ Joint Processor. The pre-processed result is then sent to the GS detector. According to simulation results, in channels with varying correlation values, the suggested iterative scheme’s BER is less than that of the GS and the improved iterative scheme based on GS. Furthermore, it can approach the BER performance of the MMSE detection algorithm with fewer iterations. The suggested technique has a computational complexity of O(U2), whereas the MMSE detection algorithm has a computational complexity of O(U3), where U is the number of users. For the same detection performance, the computational complexity of the proposed algorithm is an order of magnitude lower than that of MMSE. With fewer iterations, the proposed algorithm achieves a better balance between detection performance and computational complexity.

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Section: Complexity Analysismentioning

confidence: 99%

Fast Converging Gauss–Seidel Iterative Algorithm for Massive MIMO Systems

Shen,

Chen,

Liang

2023

Applied Sciences

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Fast Converging Gauss-Seidel Iterative Algorithm for Massive MIMO Systems

Shen,

Chen,

et al. 2023

Preprint

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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.

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A New Randomized Iterative Detection Algorithm for Uplink Large-Scale MIMO Systems

Cited by 2 publications

References 37 publications

Fast Converging Gauss–Seidel Iterative Algorithm for Massive MIMO Systems

Fast Converging Gauss–Seidel Iterative Algorithm for Massive MIMO Systems

Fast Converging Gauss-Seidel Iterative Algorithm for Massive MIMO Systems

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