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. 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. We reduce complexity by converting a large matrix inversion to small matrix inversions. In order to improve the symbol error ratio (SER) of BGS-Net under MAUE system, we propose Improved BGS-Net. 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.

The traditional Gauss-Seidel iterative method runs in parallel with high complexity. To reduce the complexity, this paper proposes a model-driven Deep learning(DL) detector network, namely Block Gauss-Seidel Network(BGS-Net), which is based on the Gauss-Seidel iterative method. We reduce complexity by converting a large matrix inversion to small matrix inversions. Single-antenna user equipment (SAUE) and multiple-antenna user equipment (MAUE) systems under Rayleigh channel are considered in this paper. In order to improve the Symbol Error Ratio(SER) of BGS-Net under MAUE system, we improve the accuracy of the initial solution of BGS-Net, called Improved BGS-Net. The simulation results show that, compared with the existing model-driven algorithms, BGS-Net has lower complexity and similar SER; good robustness, and its performance is less affected by changes in the number of antennas; SER is better than traditional Gauss-Seidel; Improved BGS-Net can improve the SER of BGS-Net.