Massive MIMO System Signal Detection Based on Convolutional Feature Extraction and Deep Denoising Network

‍ ‍The performance of traditional multiple-input multiple-output （MIMO） signal detection algorithms is influenced by factors such as the number of antennas and the ratio of transmitting to receiving antennas. Therefore， it applies to scenarios with a few antennas and a low ratio of antennas. This study proposes a DL-based sparsely connected convolutional denoising network model for uplink signal detection in a massive MIMO system. First， by simplifying the classical detection network （DetNet）， improving the detection algorithm sparsely connected neural network （ScNet）， and introducing convolutional neural networks （CNNs） to extract features from three-channel input data and reduce training parameters， this paper proposes a detection algorithm called sparsely connected convolutional neural network （SConv）. SConv enhances detection accuracy and reduces computational complexity compared to DetNet. Building on this， the paper proposes the SConv-D denoising network， a convolutional denoising neural network-assisted massive MIMO detection algorithm， by integrating a CNN-based signal denoising module within the SConv framework. The detection process of this algorithm is divided into two stages. The first stage is provided by the SConv algorithm for the initial solution. Subsequently， the initial solution is used as the input of the denoising process， which constitutes the second stage of the algorithm. Simulation results show that the SConv-D algorithm is suitable for QPSK， 4QAM， and 16QAM signal modulation models， and the performance gain obtained in the high-order modulation mode is particularly apparent. In addition， this algorithm can adapt to system configurations with different proportions of the receiving and transmitting antennas and different numbers of antennas. Particularly when the number of receiving and transmitting antennas is equal， SConv-D can also achieve better performance. The algorithm also overcomes the performance plateau effect of MMNet under high-order modulation scenarios. Compared with MMNet， a gain of approximately 2 dB is achieved in bit error rate when employing 16QAM modulation， while the number of receiving and transmitting antennas remain the same.