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.

在具有单天线用户设备（SAUE）或多天线用户设备（MAUE）的大规模多输入多输出（MIMO）系统中，随着基站接收天线数量的增加，传统检测器的复杂度越来越高。也在增加。为了降低传统Gauss-Seidel迭代方法并行运行的高复杂度，本文提出了一种模型驱动的深度学习检测器网络，即Block Gauss-Seidel Network (BGS-Net)，它基于Gauss-赛德尔迭代法。我们通过将大矩阵求逆转换为小矩阵求逆来降低复杂度。为了提高MAUE系统下BGS-Net的符号错误率（SER），我们提出了改进的BGS-Net。仿真结果表明，与现有的模型驱动算法相比，BGS-Net 复杂度较低，检测性能相似；鲁棒性好，其性能受天线数量变化的影响较小；改进的 BGS-Net 可以提高 BGS-Net 的检测性能。