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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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Published in: | IEEE wireless communications letters 2018-08, Vol.7 (4), p.634-637 |
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creator | He, Dongxuan Liu, Chenxi Quek, Tony Q. S. Wang, Hua |
description | 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. |
doi_str_mv | 10.1109/LWC.2018.2805902 |
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S. ; Wang, Hua</creator><creatorcontrib>He, Dongxuan ; Liu, Chenxi ; Quek, Tony Q. S. ; Wang, Hua</creatorcontrib><description>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.</description><identifier>ISSN: 2162-2337</identifier><identifier>EISSN: 2162-2345</identifier><identifier>DOI: 10.1109/LWC.2018.2805902</identifier><identifier>CODEN: IWCLAF</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>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</subject><ispartof>IEEE wireless communications letters, 2018-08, Vol.7 (4), p.634-637</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c357t-5cea16eaaa11e0d284456ee746a17b913513e9c71926004820a6e5ba526ff9a33</citedby><cites>FETCH-LOGICAL-c357t-5cea16eaaa11e0d284456ee746a17b913513e9c71926004820a6e5ba526ff9a33</cites><orcidid>0000-0002-3660-4290 ; 0000-0002-4037-3149 ; 0000-0002-5429-3318 ; 0000-0002-9134-1235</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8291154$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,786,790,27957,27958,55147</link.rule.ids></links><search><creatorcontrib>He, Dongxuan</creatorcontrib><creatorcontrib>Liu, Chenxi</creatorcontrib><creatorcontrib>Quek, Tony Q. 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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.</description><subject>Antennas</subject><subject>Artificial intelligence</subject><subject>Bayesian analysis</subject><subject>Channels</subject><subject>Machine learning</subject><subject>MIMO communication</subject><subject>naive-Bayes</subject><subject>Network security</subject><subject>Physical layer</subject><subject>physical layer security</subject><subject>support vector machine</subject><subject>Support vector machines</subject><subject>Training</subject><subject>transmit antenna selection</subject><subject>Transmitting antennas</subject><subject>Wiretapping</subject><issn>2162-2337</issn><issn>2162-2345</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNo9kEtrwzAQhEVpoSHNvdCLoGenWsmSrd6M6SPgkEJTchSKu24UEtmVnEP_fR0Sspddhpkd-Ai5BzYFYPqpWpVTziCf8pxJzfgVGXFQPOEildeXW2S3ZBLjlg2jGHDIR-RjGayPe9fTwvfovaWfuMO6d62nztP5bL6gKxewtx0tN9Z73MVnWtC5rTfOI63QBu_8Dy26LrSDeEduGruLODnvMfl6fVmW70m1eJuVRZXUQmZ9Imu0oNBaC4Dsm-dpKhVilioL2VqDkCBQ1xlorhhLc86sQrm2kqum0VaIMXk8_R1qfw8Ye7NtD8EPlYYDZJBqqbPBxU6uOrQxBmxMF9zehj8DzBzRmQGdOaIzZ3RD5OEUcYh4sedcA8hU_AOSe2ep</recordid><startdate>20180801</startdate><enddate>20180801</enddate><creator>He, Dongxuan</creator><creator>Liu, Chenxi</creator><creator>Quek, Tony Q. 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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.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/LWC.2018.2805902</doi><tpages>4</tpages><orcidid>https://orcid.org/0000-0002-3660-4290</orcidid><orcidid>https://orcid.org/0000-0002-4037-3149</orcidid><orcidid>https://orcid.org/0000-0002-5429-3318</orcidid><orcidid>https://orcid.org/0000-0002-9134-1235</orcidid></addata></record> |
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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 |
title | Transmit Antenna Selection in MIMO Wiretap Channels: A Machine Learning Approach |
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