A DNN Architecture for the Detection of Generalized Spatial Modulation Signals

Abstract

In this letter, we consider the problem of signal detection in generalized spatial modulation (GSM) using deep neural networks (DNN). We propose a novel modularized DNN architecture that uses small sub-DNNs to detect the active antennas and complex modulation symbols, instead of using a single large DNN to jointly detect the active antennas and modulation symbols. The main idea is that using small sub-DNNs instead of a single large DNN reduces the required size of the NN and hence requires learning lesser number of parameters. Under the assumption of i.i.d Gaussian noise, the proposed DNN detector achieves a performance very close to that of the maximum likelihood detector. We also analyze the performance of the proposed detector under two practical conditions: i) correlated noise across receive antennas and ii) noise distribution deviating from the standard Gaussian model. The proposed DNN-based detector learns the deviations from the standard model and achieves superior performance compared to that of the conventional maximum likelihood detector.