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Low-Complexity Joint Weighted Neumann Series and Gauss–Seidel Soft-Output Detection for Massive MIMO Systems

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Abstract

We introduce a joint weighted Neumann series (WNS) and Gauss–Seidel (GS) approach to implement an approximated linear minimum mean-squared error (LMMSE) detector for uplink massive multiple-input multiple-output (M-MIMO) systems. We first propose to initialize the GS iteration by a WNS method, which produces a closer-to-LMMSE initial solution than the conventional zero vector and diagonal-matrix based scheme. Then the GS algorithm is applied to implement an approximated LMMSE detection iteratively. Furthermore, based on the WNS, we devise a low-complexity approximate log-likelihood ratios (LLRs) computation method whose performance loss is negligible compared with the exact method. Numerical results illustrate that the proposed joint WNS-GS approach outperforms the conventional method and achieves near-LMMSE performance with significantly lower computational complexity.

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Authors and Affiliations

  1. University of Science and Technology Beijing, Beijing, 100030, China

    Xiaoming Dai, Tiantian Yan, Yuanyuan Dong, Yuquan Luo & Hua Li

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  1. Xiaoming Dai
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  2. Tiantian Yan
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  3. Yuanyuan Dong
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  4. Yuquan Luo
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  5. Hua Li
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Correspondence to Xiaoming Dai.

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Dai, X., Yan, T., Dong, Y. et al. Low-Complexity Joint Weighted Neumann Series and Gauss–Seidel Soft-Output Detection for Massive MIMO Systems. Wireless Pers Commun 120, 2801–2811 (2021). https://doi.org/10.1007/s11277-021-08557-2

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  • DOI: https://doi.org/10.1007/s11277-021-08557-2

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