Complex-Valued Neural Network Detection for RIS-Assisted Generalized Spatial Modulation

Applying the deep learning in signal processing for communication systems, several models based on Real-Valued Deep Neural Network (RVDNN) and convolutional neural network (RVCNN) have been previously proposed to detect signals of generalized spatial modulation. This paper proposes a complex-valued deep neural network (CVDNN) and a complex-valued convolutional neural network (CVCNN) as detectors for reconfigurable intelligent surface (RIS)-assisted generalized spatial modulation. Contrary to previous models, the complex-valued signals are directly fed into the neural network, which requires few feature vector generators and therefore has a simpler structure. Simulation results show that the proposed CVNN detectors exhibit improved error performance and stability for various modulation schemes compared with other traditional detection schemes over Nakagami-m fading channels. The results are shown to be approaching that of maximum likelihood detection, while outperforming existing RVNN detectors.