Securing Transmission for UAV Swarm-Enabled Communication Network
Deploying unmanned aerial vehicles (UAVs) in communication networks could achieve desirable degrees of freedom, but resulting from the high line-of-sight link attributes, security threats faced by UAV communications are serious. Taking that into consideration, we focus on a UAV swarm-enabled communi...
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Veröffentlicht in: | IEEE systems journal 2022-12, Vol.16 (4), p.5200-5211 |
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creator | Dong, Runze Wang, Buhong Cao, Kunrui Cheng, Tianhao |
description | Deploying unmanned aerial vehicles (UAVs) in communication networks could achieve desirable degrees of freedom, but resulting from the high line-of-sight link attributes, security threats faced by UAV communications are serious. Taking that into consideration, we focus on a UAV swarm-enabled communication network in this article, where a fixed-wing UAV acts as a legitimate user and a neighboring UAV eavesdropper attempts to eavesdrop the confidential information forwarded by rotary-wing UAV relays. In particular, we jointly optimize transmit power on the base station and UAV relays, power allocation coefficient and beamforming on UAV relays, and trajectory of the fixed-wing UAV to maximize the average secrecy rate. Due to the nonconvexity of the optimization problem, the block coordinate descent method is utilized to solve it. Specifically, the overall optimizing problem is divided into four subproblems in which the transmit power, power allocation coefficient, beamforming vector, and the trajectory of user are optimized, respectively. By employing the successive convex approximate method, subproblems are transformed to convex problems which are numerically tractable. Numerical simulations validate the effectiveness of the proposed algorithm and show that compared with benchmark schemes, the considered transmission scheme with artificial noise and optimized network parameters can improve secrecy rate by about 13.8%. |
doi_str_mv | 10.1109/JSYST.2021.3111746 |
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Taking that into consideration, we focus on a UAV swarm-enabled communication network in this article, where a fixed-wing UAV acts as a legitimate user and a neighboring UAV eavesdropper attempts to eavesdrop the confidential information forwarded by rotary-wing UAV relays. In particular, we jointly optimize transmit power on the base station and UAV relays, power allocation coefficient and beamforming on UAV relays, and trajectory of the fixed-wing UAV to maximize the average secrecy rate. Due to the nonconvexity of the optimization problem, the block coordinate descent method is utilized to solve it. Specifically, the overall optimizing problem is divided into four subproblems in which the transmit power, power allocation coefficient, beamforming vector, and the trajectory of user are optimized, respectively. By employing the successive convex approximate method, subproblems are transformed to convex problems which are numerically tractable. Numerical simulations validate the effectiveness of the proposed algorithm and show that compared with benchmark schemes, the considered transmission scheme with artificial noise and optimized network parameters can improve secrecy rate by about 13.8%.</description><identifier>ISSN: 1932-8184</identifier><identifier>EISSN: 1937-9234</identifier><identifier>DOI: 10.1109/JSYST.2021.3111746</identifier><identifier>CODEN: ISJEB2</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Antenna arrays ; Array signal processing ; Artificial noise (AN) ; Autonomous aerial vehicles ; Base stations ; Beamforming ; block coordinate descent ; Communication ; Communication networks ; Communications networks ; Eavesdropping ; Fixed wings ; Line of sight communication ; Optimization ; Particle swarm optimization ; physical layer security ; Relays ; Trajectory ; trajectory optimization ; unmanned aerial vehicle (UAV) swarm ; Unmanned aerial vehicles</subject><ispartof>IEEE systems journal, 2022-12, Vol.16 (4), p.5200-5211</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c295t-b56e85bf68fc34b1cfcbc75094804ee4c80ee2c6b0c42fea4eebc2a46c7da2763</citedby><cites>FETCH-LOGICAL-c295t-b56e85bf68fc34b1cfcbc75094804ee4c80ee2c6b0c42fea4eebc2a46c7da2763</cites><orcidid>0000-0002-7737-4639 ; 0000-0002-7261-8331 ; 0000-0003-1950-6901 ; 0000-0001-7425-0502</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9552611$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,778,782,794,27907,27908,54741</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9552611$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Dong, Runze</creatorcontrib><creatorcontrib>Wang, Buhong</creatorcontrib><creatorcontrib>Cao, Kunrui</creatorcontrib><creatorcontrib>Cheng, Tianhao</creatorcontrib><title>Securing Transmission for UAV Swarm-Enabled Communication Network</title><title>IEEE systems journal</title><addtitle>JSYST</addtitle><description>Deploying unmanned aerial vehicles (UAVs) in communication networks could achieve desirable degrees of freedom, but resulting from the high line-of-sight link attributes, security threats faced by UAV communications are serious. Taking that into consideration, we focus on a UAV swarm-enabled communication network in this article, where a fixed-wing UAV acts as a legitimate user and a neighboring UAV eavesdropper attempts to eavesdrop the confidential information forwarded by rotary-wing UAV relays. In particular, we jointly optimize transmit power on the base station and UAV relays, power allocation coefficient and beamforming on UAV relays, and trajectory of the fixed-wing UAV to maximize the average secrecy rate. Due to the nonconvexity of the optimization problem, the block coordinate descent method is utilized to solve it. Specifically, the overall optimizing problem is divided into four subproblems in which the transmit power, power allocation coefficient, beamforming vector, and the trajectory of user are optimized, respectively. By employing the successive convex approximate method, subproblems are transformed to convex problems which are numerically tractable. Numerical simulations validate the effectiveness of the proposed algorithm and show that compared with benchmark schemes, the considered transmission scheme with artificial noise and optimized network parameters can improve secrecy rate by about 13.8%.</description><subject>Algorithms</subject><subject>Antenna arrays</subject><subject>Array signal processing</subject><subject>Artificial noise (AN)</subject><subject>Autonomous aerial vehicles</subject><subject>Base stations</subject><subject>Beamforming</subject><subject>block coordinate descent</subject><subject>Communication</subject><subject>Communication networks</subject><subject>Communications networks</subject><subject>Eavesdropping</subject><subject>Fixed wings</subject><subject>Line of sight communication</subject><subject>Optimization</subject><subject>Particle swarm optimization</subject><subject>physical layer security</subject><subject>Relays</subject><subject>Trajectory</subject><subject>trajectory optimization</subject><subject>unmanned aerial vehicle (UAV) swarm</subject><subject>Unmanned aerial vehicles</subject><issn>1932-8184</issn><issn>1937-9234</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kF1LwzAUhoMoOKd_QG8KXrcmaZIml2PML4ZedBO8Ckl2Kp1rM5OW4b-3-8Cr83J43nPgQeiW4IwQrB5ey89ykVFMSZYTQgomztCIqLxIFc3Z-SHTVBLJLtFVjGuMueSFGqFJCa4PdfuVLIJpY1PHWPs2qXxIlpOPpNyZ0KSz1tgNrJKpb5q-rZ3p9swbdDsfvq_RRWU2EW5Oc4yWj7PF9Dmdvz-9TCfz1FHFu9RyAZLbSsjK5cwSVznrCo4Vk5gBMCcxAHXCYsdoBWbYWUcNE65YGVqIfIzuj3e3wf_0EDu99n1oh5eaFkxywSVTA0WPlAs-xgCV3oa6MeFXE6z3qvRBld6r0idVQ-nuWKoB4L-gOKeCkPwP049mGw</recordid><startdate>202212</startdate><enddate>202212</enddate><creator>Dong, Runze</creator><creator>Wang, Buhong</creator><creator>Cao, Kunrui</creator><creator>Cheng, Tianhao</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><orcidid>https://orcid.org/0000-0002-7737-4639</orcidid><orcidid>https://orcid.org/0000-0002-7261-8331</orcidid><orcidid>https://orcid.org/0000-0003-1950-6901</orcidid><orcidid>https://orcid.org/0000-0001-7425-0502</orcidid></search><sort><creationdate>202212</creationdate><title>Securing Transmission for UAV Swarm-Enabled Communication Network</title><author>Dong, Runze ; Wang, Buhong ; Cao, Kunrui ; Cheng, Tianhao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c295t-b56e85bf68fc34b1cfcbc75094804ee4c80ee2c6b0c42fea4eebc2a46c7da2763</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Antenna arrays</topic><topic>Array signal processing</topic><topic>Artificial noise (AN)</topic><topic>Autonomous aerial vehicles</topic><topic>Base stations</topic><topic>Beamforming</topic><topic>block coordinate descent</topic><topic>Communication</topic><topic>Communication networks</topic><topic>Communications networks</topic><topic>Eavesdropping</topic><topic>Fixed wings</topic><topic>Line of sight communication</topic><topic>Optimization</topic><topic>Particle swarm optimization</topic><topic>physical layer security</topic><topic>Relays</topic><topic>Trajectory</topic><topic>trajectory optimization</topic><topic>unmanned aerial vehicle (UAV) swarm</topic><topic>Unmanned aerial vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dong, Runze</creatorcontrib><creatorcontrib>Wang, Buhong</creatorcontrib><creatorcontrib>Cao, Kunrui</creatorcontrib><creatorcontrib>Cheng, Tianhao</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 (IEL)</collection><collection>CrossRef</collection><jtitle>IEEE systems journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Dong, Runze</au><au>Wang, Buhong</au><au>Cao, Kunrui</au><au>Cheng, Tianhao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Securing Transmission for UAV Swarm-Enabled Communication Network</atitle><jtitle>IEEE systems journal</jtitle><stitle>JSYST</stitle><date>2022-12</date><risdate>2022</risdate><volume>16</volume><issue>4</issue><spage>5200</spage><epage>5211</epage><pages>5200-5211</pages><issn>1932-8184</issn><eissn>1937-9234</eissn><coden>ISJEB2</coden><abstract>Deploying unmanned aerial vehicles (UAVs) in communication networks could achieve desirable degrees of freedom, but resulting from the high line-of-sight link attributes, security threats faced by UAV communications are serious. Taking that into consideration, we focus on a UAV swarm-enabled communication network in this article, where a fixed-wing UAV acts as a legitimate user and a neighboring UAV eavesdropper attempts to eavesdrop the confidential information forwarded by rotary-wing UAV relays. In particular, we jointly optimize transmit power on the base station and UAV relays, power allocation coefficient and beamforming on UAV relays, and trajectory of the fixed-wing UAV to maximize the average secrecy rate. Due to the nonconvexity of the optimization problem, the block coordinate descent method is utilized to solve it. Specifically, the overall optimizing problem is divided into four subproblems in which the transmit power, power allocation coefficient, beamforming vector, and the trajectory of user are optimized, respectively. By employing the successive convex approximate method, subproblems are transformed to convex problems which are numerically tractable. Numerical simulations validate the effectiveness of the proposed algorithm and show that compared with benchmark schemes, the considered transmission scheme with artificial noise and optimized network parameters can improve secrecy rate by about 13.8%.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/JSYST.2021.3111746</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-7737-4639</orcidid><orcidid>https://orcid.org/0000-0002-7261-8331</orcidid><orcidid>https://orcid.org/0000-0003-1950-6901</orcidid><orcidid>https://orcid.org/0000-0001-7425-0502</orcidid></addata></record> |
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subjects | Algorithms Antenna arrays Array signal processing Artificial noise (AN) Autonomous aerial vehicles Base stations Beamforming block coordinate descent Communication Communication networks Communications networks Eavesdropping Fixed wings Line of sight communication Optimization Particle swarm optimization physical layer security Relays Trajectory trajectory optimization unmanned aerial vehicle (UAV) swarm Unmanned aerial vehicles |
title | Securing Transmission for UAV Swarm-Enabled Communication Network |
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