Generalized spatial modulation detector assisted by reconfigurable intelligent surface based on deep learning

Abstract

Reconfigurable Intelligent Surface (RIS) is regarded as a cutting-edge technology for the development of future wireless communication networks with improved frequency efficiency and reduced energy consumption. This paper proposes an architecture by combining RIS with Generalized Spatial Modulation (GSM) and then presents a Multi-Residual Deep Neural Network (MR-DNN) scheme, where the active antennas and their transmitted constellation symbols are detected by sub-DNNs in the detection block. Simulation results demonstrate that the proposed MR-DNN detection algorithm performs considerably better than the traditional Zero-Forcing (ZF) and the Minimum Mean Squared Error (MMSE) detection algorithms in terms of Bit Error Rate (BER). Moreover, the MR-DNN detection algorithm has less time complexity than the traditional detection algorithms.