As a green and secure wireless transmission method, secure spatial modulation (SM) is becoming a hot research area. Its basic idea is to exploit both the index of activated transmit antenna and amplitude phase modulation signal to carry messages, improve security, and save energy. In this paper, we review its crucial challenges: transmit antenna selection (TAS), artificial noise (AN) projection, power allocation (PA) and joint detection at the desired receiver. As the size of signal constellation tends to medium-scale or large-scale, the complexity of traditional maximum likelihood detector becomes prohibitive. To reduce this complexity, a low-complexity maximum likelihood (ML) detector is proposed. To further enhance the secrecy rate (SR) performance, a deep-neural-network (DNN) PA strategy is proposed. Simulation results show that the proposed low-complexity ML detector, with a lower-complexity, has the same bit error rate performance as the joint ML method while the proposed DNN method strikes a good balance between complexity and SR performance.
翻译:作为绿色和安全的无线传输方法,安全的空间调制(SM)正在成为一个热研究领域,其基本想法是利用激活传输天线和振动阶段调制信号的索引来传递信息,加强安全和节能。在本文件中,我们审查了其关键挑战:传输天线选择(TAS)、人工噪音(AN)投射、电源分配(PA)和对理想接收器的联合探测。由于信号星座的大小倾向于中尺度或大尺度,传统最大可能性探测器的复杂程度变得令人望而却步。为降低这一复杂性,提出了低复杂性最大可能性探测器(ML)的建议。为进一步提高保密率(SR)性能,提出了深神经网络(DNNN)PA战略。模拟结果表明,低兼容性ML探测器的建议与联合 ML 方法的误率性能是相同的,而拟议的DNN方法则在复杂性和SR性能之间达到良好的平衡。