Passive Beamforming Design and DNN-Based Signal Detection in RIS-Assisted MIMO Systems With Generalized Spatial Modulation

Reconfigurable Intelligent Surfaces (RISs) have recently gained significant attention as they enable controlling the wireless propagation environment to improve information transmission with the help of reflective elements. This paper studies a RIS-assisted generalized spatial modulation (GSM) multiple input multiple output (MIMO) system to improve the signal quality and spectral efficiency. Due to inter-channel interference, signal detection in GSM becomes a critical issue. Therefore, we propose a low complexity block deep neural network (DNN) detector for the RIS-GSM MIMO system to detect the active antennas and the transmitted symbols at the receiver. In order to optimize the phase shifts at the RIS elements, we propose cosine similarity-based and semidefinite relaxation (SDR)-based algorithms. Further, conventional signal detectors such as maximum likelihood (ML), block zero-forcing (B-ZF), and block minimum mean square error (B-MMSE) are applied. In addition, we explore and analyze the performance of the proposed framework in terms of bit error rate (BER) and time complexity. Numerical results reveal that both optimize-phase-shifts algorithms assisted DNN signal detector has almost the same BER performance as ML, but it is four times faster than the linear detector (B-ZF and B-MMSE), and seven times faster than ML. Clearly, this scheme outperforms the alternative traditional signal detectors with the least time complexity.