Deep Learning Based Signal Detection for Quadrature Spatial Modulation System  

作　　者：Shu Dingyun Peng Yuyang Yue Ming Fawaz AL-Hazemi Mohammad Meraj Mirza 

机构地区：[1]School of Computer Science and Engineering,Macao University of Science and Technology,Macao,China [2]Department of Computer and Network Engineering,University of Jeddah,Saudi Arabia [3]Department of Computer Science,College of Computers and Information Technology,Taif University,P.O.Box 11099,Taif 21944,Saudi Arabia

出　　处：《China Communications》2024年第10期78-85,共8页中国通信（英文版）

基　　金：supported in part by The Science and Technology Development Fund, Macao SAR, China (0108/2020/A3);in part by The Science and Technology Development Fund, Macao SAR, China (0005/2021/ITP);the Deanship of Scientific Research at Taif University for funding this work。

摘　　要：With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. In QSM, the traditional signal detection methods sometimes are unable to meet the actual requirement of low complexity of the system. Therefore, this paper proposes a signal detection scheme for QSM systems using deep learning to solve the complexity problem. Results from the simulations show that the bit error rate performance of the proposed deep learning-based detector is better than that of the zero-forcing(ZF) and minimum mean square error(MMSE) detectors, and similar to the maximum likelihood(ML) detector. Moreover, the proposed method requires less processing time than ZF, MMSE,and ML.

关 键 词：bit error rate COMPLEXITY deep learning quadrature spatial modulation 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TN911.23[自动化与计算机技术—控制科学与工程]