![]() The simulation results show that, compared with the existing model-driven algorithms, BGS-Net has lower complexity and similar the detection performance good robustness, and its performance is less affected by changes in the number of antennas Improved BGS-Net can improve the detection performance of BGS-Net.īeyond fifth generation (B5G) is a perfect process to solve some application scenarios and technologies of 5th generation mobile communication technology (5G). In order to improve the symbol error ratio (SER) of BGS-Net under MAUE system, we propose Improved BGS-Net. We reduce complexity by converting a large matrix inversion to small matrix inversions. In order to reduce the high complexity of parallel running of the traditional Gauss-Seidel iterative method, this paper proposes a model-driven deep learning detector network, namely Block Gauss-Seidel Network (BGS-Net), which is based on the Gauss-Seidel iterative method. In massive multiple-input multiple-output (MIMO) systems with single- antenna user equipment (SAUE) or multiple-antenna user equipment (MAUE), with the increase of the number of received antennas at base station, the complexity of traditional detectors is also increasing.
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