Deep Learning Based Antenna Selection Technique for RIS-Empowered RQSM System

TL;DR

This paper introduces a deep learning-based antenna selection method combined with capacity-optimized antenna selection to enhance error performance in RIS-empowered RQSM systems, demonstrating improved accuracy and analyzing complexity trade-offs.

Contribution

It proposes a novel DNN-supported antenna selection approach integrated with COAS for RIS-RQSM systems, improving error performance over traditional methods.

Findings

DNN-COAS-RIS-RQSM outperforms traditional COAS in error metrics.

The proposed method shows competitive computational complexity.

Simulation results validate the effectiveness of the deep learning approach.

Abstract

Reconfigurable intelligent surface (RIS) technology has attracted considerable interest due to its ability to control wireless propagation with minimal power usage. Receive quadrature spatial modulation (RQSM) scheme transmits data bits in both in-phase ($I$) and quadrature ($Q$) channels, doubling the number of active receive antenna indices and improving spectral efficiency compared to the traditional receive spatial modulation (RSM) technique. Also, capacity-optimized antenna selection (COAS) improves error performance by selecting antennas with the best channel conditions. This paper proposes a new deep neural network (DNN)-based antenna selection method, supported by the COAS technique, to improve the error performance of the RIS-RQSM system. Monte Carlo simulations of the proposed DNN-COAS-RIS-RQSM system using the quadrature amplitude modulation (QAM) technique for Rayleigh fading channels are performed and compared with the COAS-RIS-RQSM system. Also, a comparative analysis of the computational complexities of the DNN and COAS techniques is conducted to evaluate the trade-offs between error performance and complexity.