Block Deep Neural Network-Based Signal Detector for Generalized Spatial Modulation

TL;DR

This paper introduces a block deep neural network architecture for signal detection in generalized spatial modulation systems, significantly improving accuracy and computational efficiency over traditional methods.

Contribution

It proposes a novel block DNN detector that enhances detection performance and reduces computation time in GSM systems compared to existing techniques.

Findings

Achieves superior BER performance over B-ZF and B-MMSE detectors.

Matches classical ML detector performance.

Requires less computation time than conventional methods.

Abstract

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.