Transmit antenna selection in MIMO wiretap channels: A machine learning approach

Dongxuan He, Chenxi Liu, Tony Q.S. Quek, Hua Wang

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number8291154
Pages (from-to)634-637
Number of pages4
JournalIEEE Wireless Communications Letters
Volume7
Issue number4
DOIs
Publication statusPublished - 2018 Aug

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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