Signal Detection for Enhanced Spatial Modulation-Based Communication: A Block Deep Neural Network Approach

As a novel variant of spatial modulation (SM), enhanced SM (ESM) provides higher spectral efficiency and improved bit error rate (BER) performance compared to SM. In ESM, traditional signal detection methods such as maximum likelihood (ML) have the drawback of high complexity. Therefore, in this paper, we try to solve this problem using a deep neural network (DNN). Specifically, we propose a block DNN (B-DNN) structure, in which smaller B-DNNs are utilized to identify the active antennas along with the constellation symbols they transmit. Simulation outcomes indicate that the BER performance related to the introduced B-DNN method outperforms both the minimum mean-square error (MMSE) and the zero-forcing (ZF) methods, approaching that of the ML method. Furthermore, the proposed method requires less computation time compared to the ML method.