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

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 strat...

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Bibliographic Details
Published in:IEEE wireless communications letters 2018-08, Vol.7 (4), p.634-637
Main Authors: He, Dongxuan, Liu, Chenxi, Quek, Tony Q. S., Wang, Hua
Format: Article
Language:English
Subjects:
Antennas
Artificial intelligence
Bayesian analysis
Channels
Machine learning
MIMO communication
naive-Bayes
Network security
Physical layer
physical layer security
support vector machine
Support vector machines
Training
transmit antenna selection
Transmitting antennas
Wiretapping
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Summary: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.
ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2018.2805902