Massive MIMO Symmetric SOR Neural Detector

In this paper, a deep learning (DL)-based detector, namely SSOR-Net, is proposed for massive MIMO systems. SSOR-Net enhances the conventional Symmetric Successive Over-Relaxation (SSOR) method while maintaining low complexity. These vectors are optimized through network training, using DL techniques to maximize the detector’s effectiveness. Extensive simulations and complexity analysis reveal that SSOR-Net achieves performance comparable to the optimal OAMP-Net while offering significantly lower complexity. Furthermore, SSOR-Net outperforms MMSE detection, traditional SSOR methods, and other DL-based schemes, particularly in scenarios where the number of transmit antennas approaches that of the receive antennas.

Massive MIMO Symmetric SOR Neural Detector

S. Pourmohammad Azizi, Shyi-Chyi Cheng, Hoang-Yang Lu, National Taiwan Ocean University