Loading…

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...

Full description

Saved in:
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:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c357t-5cea16eaaa11e0d284456ee746a17b913513e9c71926004820a6e5ba526ff9a33
cites cdi_FETCH-LOGICAL-c357t-5cea16eaaa11e0d284456ee746a17b913513e9c71926004820a6e5ba526ff9a33
container_end_page 637
container_issue 4
container_start_page 634
container_title IEEE wireless communications letters
container_volume 7
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
format article
fullrecord <record><control><sourceid>proquest_ieee_</sourceid><recordid>TN_cdi_ieee_primary_8291154</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8291154</ieee_id><sourcerecordid>2117149597</sourcerecordid><originalsourceid>FETCH-LOGICAL-c357t-5cea16eaaa11e0d284456ee746a17b913513e9c71926004820a6e5ba526ff9a33</originalsourceid><addsrcrecordid>eNo9kEtrwzAQhEVpoSHNvdCLoGenWsmSrd6M6SPgkEJTchSKu24UEtmVnEP_fR0Sspddhpkd-Ai5BzYFYPqpWpVTziCf8pxJzfgVGXFQPOEildeXW2S3ZBLjlg2jGHDIR-RjGayPe9fTwvfovaWfuMO6d62nztP5bL6gKxewtx0tN9Z73MVnWtC5rTfOI63QBu_8Dy26LrSDeEduGruLODnvMfl6fVmW70m1eJuVRZXUQmZ9Imu0oNBaC4Dsm-dpKhVilioL2VqDkCBQ1xlorhhLc86sQrm2kqum0VaIMXk8_R1qfw8Ye7NtD8EPlYYDZJBqqbPBxU6uOrQxBmxMF9zehj8DzBzRmQGdOaIzZ3RD5OEUcYh4sedcA8hU_AOSe2ep</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2117149597</pqid></control><display><type>article</type><title>Transmit Antenna Selection in MIMO Wiretap Channels: A Machine Learning Approach</title><source>IEEE Electronic Library (IEL) Journals</source><creator>He, Dongxuan ; Liu, Chenxi ; Quek, Tony Q. 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. S.</creatorcontrib><creatorcontrib>Wang, Hua</creatorcontrib><title>Transmit Antenna Selection in MIMO Wiretap Channels: A Machine Learning Approach</title><title>IEEE wireless communications letters</title><addtitle>LWC</addtitle><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><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. S.</creator><creator>Wang, Hua</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><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></search><sort><creationdate>20180801</creationdate><title>Transmit Antenna Selection in MIMO Wiretap Channels: A Machine Learning Approach</title><author>He, Dongxuan ; Liu, Chenxi ; Quek, Tony Q. S. ; Wang, Hua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c357t-5cea16eaaa11e0d284456ee746a17b913513e9c71926004820a6e5ba526ff9a33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Antennas</topic><topic>Artificial intelligence</topic><topic>Bayesian analysis</topic><topic>Channels</topic><topic>Machine learning</topic><topic>MIMO communication</topic><topic>naive-Bayes</topic><topic>Network security</topic><topic>Physical layer</topic><topic>physical layer security</topic><topic>support vector machine</topic><topic>Support vector machines</topic><topic>Training</topic><topic>transmit antenna selection</topic><topic>Transmitting antennas</topic><topic>Wiretapping</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>He, Dongxuan</creatorcontrib><creatorcontrib>Liu, Chenxi</creatorcontrib><creatorcontrib>Quek, Tony Q. S.</creatorcontrib><creatorcontrib>Wang, Hua</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE Electronic Library Online</collection><collection>CrossRef</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE wireless communications letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>He, Dongxuan</au><au>Liu, Chenxi</au><au>Quek, Tony Q. S.</au><au>Wang, Hua</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Transmit Antenna Selection in MIMO Wiretap Channels: A Machine Learning Approach</atitle><jtitle>IEEE wireless communications letters</jtitle><stitle>LWC</stitle><date>2018-08-01</date><risdate>2018</risdate><volume>7</volume><issue>4</issue><spage>634</spage><epage>637</epage><pages>634-637</pages><issn>2162-2337</issn><eissn>2162-2345</eissn><coden>IWCLAF</coden><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.</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>
fulltext fulltext
identifier ISSN: 2162-2337
ispartof IEEE wireless communications letters, 2018-08, Vol.7 (4), p.634-637
issn 2162-2337
2162-2345
language eng
recordid cdi_ieee_primary_8291154
source IEEE Electronic Library (IEL) Journals
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
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-09-22T00%3A24%3A52IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_ieee_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Transmit%20Antenna%20Selection%20in%20MIMO%20Wiretap%20Channels:%20A%20Machine%20Learning%20Approach&rft.jtitle=IEEE%20wireless%20communications%20letters&rft.au=He,%20Dongxuan&rft.date=2018-08-01&rft.volume=7&rft.issue=4&rft.spage=634&rft.epage=637&rft.pages=634-637&rft.issn=2162-2337&rft.eissn=2162-2345&rft.coden=IWCLAF&rft_id=info:doi/10.1109/LWC.2018.2805902&rft_dat=%3Cproquest_ieee_%3E2117149597%3C/proquest_ieee_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c357t-5cea16eaaa11e0d284456ee746a17b913513e9c71926004820a6e5ba526ff9a33%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2117149597&rft_id=info:pmid/&rft_ieee_id=8291154&rfr_iscdi=true