Low-complexity signal detection networks based on Gauss-Seidel iterative method for massive MIMO systems

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.

Automated summary

This paper proposes a model-driven deep learning detector network called Block Gauss-Seidel Network (BGS-Net) based on the Gauss-Seidel iterative method to reduce the complexity in massive MIMO systems. By converting a large matrix inversion to small matrix inversions, BGS-Net achieves lower complexity and good robustness. Improved BGS-Net is able to further enhance the detection performance.