Optimizing MIMO Detection with DM-Detnet in 6G Networks

Artificial intelligence (AI) has transformed multiple inputs multiple output (MIMO) technology into a key enabling technology for beyond fifth-generation (B5G) technology such as sixth-generation (6G) networks. However, MIMO technology faces some crucial research challenges, among which signal detection is a significant problem. This paper presents an optimized deep learning-based MIMO detection method called deep MIMO detection network (DM-Detnet) for MIMO detection. The light network architecture of DM-Detnet allows signal detection to be performed in a layer-by-layer manner. This work primarily concentrates on the effect of signal-to-noise ratio (SNR) points on network training and testing. We conducted a detailed simulation study to analyze the performance of our model based on specific low and high SNR points. With intensive training and optimization, the DM-Detnet model achieves better performance in MIMO scenarios. Simulation results show that the optimized DM-Detnet achieves better symbol error rate (SER) performance and is also able to achieve lower computational complexity than benchmark conventional MIMO detectors.