Efficient Dual-Mode Generalized Spatial Modulation Detection with Enhanced DNN Architecture

Abstract

Dual-mode generalized spatial modulation (DM-GSM) enhances spectral efficiency in GSM systems using two modes across transmit antennas. However, interference between antennas poses a challenge for signal detection. For this, a deep learning detector, the dual-mode deep neural network (DM-DNN), is proposed. The DM-DNN enables simultaneous detection of the antenna mode and modulation symbol through its network structure and label generation. A loss function is proposed to train the DM-DNN, approximating optimal bit error rate (BER) performance. Simulation results demonstrate that the DM-DNN achieves BER performance close to the maximum likelihood (ML) detector while significantly reducing complexity.