  Generalized Spatial Modulation (GSM) is being considered for high capacity
and energy-efficient networks of the future. However, signal detection due to
inter-channel interference among the active antennas is a challenge in GSM
systems and is the focus of this letter. Specifically, we explore the
feasibility of using deep neural networks (DNN) for signal detection in GSM. In
particular, we propose a block DNN (B-DNN) based architecture, where the active
antennas and their transmitted constellation symbols are detected by smaller
sub-DNNs. After N-ordinary DNN detection, the Euclidean distance-based soft
constellation algorithm is implemented. The proposed B-DNN detector achieves a
BER performance that is superior to traditional block zero-forcing (B-ZF) and
block minimum mean-squared error (B-MMSE) detection schemes and similar to that
of classical maximum likelihood (ML) detector. Further, the proposed method
requires less computation time and is more accurate than alternative
conventional numerical methods.
