Transmit Antenna Selection in MIMO Wiretap Channels: A Machine Learning Approach

被引:74
作者
He, Dongxuan [1 ]
Liu, Chenxi [2 ]
Quek, Tony Q. S. [2 ]
Wang, Hua [1 ]
机构
[1] Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China
[2] Singapore Univ Technol & Design, Singapore 487372, Singapore
基金
中国国家自然科学基金;
关键词
Machine learning; physical layer security; transmit antenna selection; support vector machine; naive-Bayes; SECURE TRANSMISSION;
D O I
10.1109/LWC.2018.2805902
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this letter, we exploit the potential benefits of machine learning in enhancing physical layer security in multi-input multi-output multi-antenna-eavesdropper wiretap channels. To this end, we focus on the scenario where the source adopts transmit antenna selection (TAS) as the transmission strategy. We assume that the channel state information (CSI) of the legitimate receiver is available to the source, while the CSI of the eavesdropper can be either known or not known at the source. By modeling the problem of TAS as a multiclass classification problem, we propose two machine learning-based schemes, namely, the support vector machine-based scheme and the naive-Bayes-based scheme, to select the optimal antenna that maximizes the secrecy performance of the considered system. Compared to the conventional TAS scheme, we show that our proposed schemes can achieve almost the same secrecy performance with relatively small feedback overhead. The work presented here provides insights into the design of new machine learning-based secure transmission schemes.
引用
收藏
页码:634 / 637
页数:4
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