Dynamic User Clustering and Optimal Power Allocation in UAV-Assisted Full-Duplex Hybrid NOMA System
This paper investigates unmanned aerial vehicles (UAVs)-assisted full-duplex (FD) non-orthogonal multiple access (NOMA) system based cellular network, aiming to improve overall sum-rate throughput of the system through dynamic user clustering, optimal UAV placement and power allocation. Since each U...
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Veröffentlicht in: | IEEE transactions on wireless communications 2022-04, Vol.21 (4), p.2573-2590 |
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description | This paper investigates unmanned aerial vehicles (UAVs)-assisted full-duplex (FD) non-orthogonal multiple access (NOMA) system based cellular network, aiming to improve overall sum-rate throughput of the system through dynamic user clustering, optimal UAV placement and power allocation. Since each UAV operates in FD mode, self-interference (SI), co-channel interference (CCI), inter-UAV interference (IUI) and intra-node interference (INI) dominate the system's performance. Consequently, we propose an unconventional two-stage dynamic user clustering for user nodes (UNs) to reduce the cross-interference in multi-UAV aided FD-NOMA system. Particularly, all UNs are initially clustered into K clusters using k-means clustering in the first stage where each cluster is served by an UAV. Furthermore, each cluster is further divided into sub-clusters and each sub-clusters are operated in FD-NOMA scheme. Finally, to control interferences, a sum-rate throughput maximization problem is formulated for each UAV to jointly optimize uplink and downlink power allocation and UAV placement. The joint optimization problem is non-convex and difficult to solve directly, for which we decoupled the original problem by addressing UAV placement and power allocation separately. We first fix the UAV position and then solve the problem iteratively using successive convex approximation (SCA) method. By utilizing brute-force search algorithm, an optimal UAV placement is later performed which corresponds to maximum possible sum-rate throughput. Simulation results demonstrate that the proposed solution for the considered FD-NOMA system outperforms the conventional schemes. |
doi_str_mv | 10.1109/TWC.2021.3113640 |
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Since each UAV operates in FD mode, self-interference (SI), co-channel interference (CCI), inter-UAV interference (IUI) and intra-node interference (INI) dominate the system's performance. Consequently, we propose an unconventional two-stage dynamic user clustering for user nodes (UNs) to reduce the cross-interference in multi-UAV aided FD-NOMA system. Particularly, all UNs are initially clustered into <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula> clusters using k-means clustering in the first stage where each cluster is served by an UAV. Furthermore, each cluster is further divided into sub-clusters and each sub-clusters are operated in FD-NOMA scheme. Finally, to control interferences, a sum-rate throughput maximization problem is formulated for each UAV to jointly optimize uplink and downlink power allocation and UAV placement. The joint optimization problem is non-convex and difficult to solve directly, for which we decoupled the original problem by addressing UAV placement and power allocation separately. We first fix the UAV position and then solve the problem iteratively using successive convex approximation (SCA) method. By utilizing brute-force search algorithm, an optimal UAV placement is later performed which corresponds to maximum possible sum-rate throughput. Simulation results demonstrate that the proposed solution for the considered FD-NOMA system outperforms the conventional schemes.</description><identifier>ISSN: 1536-1276</identifier><identifier>EISSN: 1558-2248</identifier><identifier>DOI: 10.1109/TWC.2021.3113640</identifier><identifier>CODEN: ITWCAX</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Approximation ; Cellular communication ; Cluster analysis ; Clustering ; Cochannel interference ; fullduplex (FD) ; Hybrid systems ; Interference ; NOMA ; non-orthogonal multiple access (NOMA) ; Nonorthogonal multiple access ; optimal UAV placement ; Optimization ; Placement ; power allocation ; Resource management ; Search algorithms ; Spectral efficiency ; sum-rate maximization ; Throughput ; Unmanned aerial vehicles ; Unmanned aerial vehicles (UAVs) ; Vector quantization ; Wireless communication</subject><ispartof>IEEE transactions on wireless communications, 2022-04, Vol.21 (4), p.2573-2590</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-d851301994c239a5f9e92b892f05980d52dfd1da5a9e30efba4a8e301b6f87383</citedby><cites>FETCH-LOGICAL-c291t-d851301994c239a5f9e92b892f05980d52dfd1da5a9e30efba4a8e301b6f87383</cites><orcidid>0000-0001-5280-384X ; 0000-0001-9028-4518 ; 0000-0003-1548-3052 ; 0000-0003-2906-9207 ; 0000-0003-0050-0921</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9547827$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9547827$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Katwe, Mayur</creatorcontrib><creatorcontrib>Singh, Keshav</creatorcontrib><creatorcontrib>Sharma, Prabhat Kumar</creatorcontrib><creatorcontrib>Li, Chih-Peng</creatorcontrib><creatorcontrib>Ding, Zhiguo</creatorcontrib><title>Dynamic User Clustering and Optimal Power Allocation in UAV-Assisted Full-Duplex Hybrid NOMA System</title><title>IEEE transactions on wireless communications</title><addtitle>TWC</addtitle><description>This paper investigates unmanned aerial vehicles (UAVs)-assisted full-duplex (FD) non-orthogonal multiple access (NOMA) system based cellular network, aiming to improve overall sum-rate throughput of the system through dynamic user clustering, optimal UAV placement and power allocation. Since each UAV operates in FD mode, self-interference (SI), co-channel interference (CCI), inter-UAV interference (IUI) and intra-node interference (INI) dominate the system's performance. Consequently, we propose an unconventional two-stage dynamic user clustering for user nodes (UNs) to reduce the cross-interference in multi-UAV aided FD-NOMA system. Particularly, all UNs are initially clustered into <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula> clusters using k-means clustering in the first stage where each cluster is served by an UAV. Furthermore, each cluster is further divided into sub-clusters and each sub-clusters are operated in FD-NOMA scheme. Finally, to control interferences, a sum-rate throughput maximization problem is formulated for each UAV to jointly optimize uplink and downlink power allocation and UAV placement. The joint optimization problem is non-convex and difficult to solve directly, for which we decoupled the original problem by addressing UAV placement and power allocation separately. We first fix the UAV position and then solve the problem iteratively using successive convex approximation (SCA) method. By utilizing brute-force search algorithm, an optimal UAV placement is later performed which corresponds to maximum possible sum-rate throughput. Simulation results demonstrate that the proposed solution for the considered FD-NOMA system outperforms the conventional schemes.</description><subject>Approximation</subject><subject>Cellular communication</subject><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Cochannel interference</subject><subject>fullduplex (FD)</subject><subject>Hybrid systems</subject><subject>Interference</subject><subject>NOMA</subject><subject>non-orthogonal multiple access (NOMA)</subject><subject>Nonorthogonal multiple access</subject><subject>optimal UAV placement</subject><subject>Optimization</subject><subject>Placement</subject><subject>power allocation</subject><subject>Resource management</subject><subject>Search algorithms</subject><subject>Spectral efficiency</subject><subject>sum-rate maximization</subject><subject>Throughput</subject><subject>Unmanned aerial vehicles</subject><subject>Unmanned aerial vehicles (UAVs)</subject><subject>Vector quantization</subject><subject>Wireless communication</subject><issn>1536-1276</issn><issn>1558-2248</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kN1LwzAUxYMoOKfvgi8Bnzvz0bTJY-mcE6YT3PQxpG0qGVlbkxbtf2_Ghk_3wPmde7kHgFuMZhgj8bD5zGcEETyjGNMkRmdgghnjESExPz9omkSYpMkluPJ-hxBOE8YmoJyPjdqbEm69djC3g--1M80XVE0F111v9srCt_YnmJm1bal60zbQNHCbfUSZ9ybwFVwM1kbzobP6Fy7HwpkKvq5fMvg-Bnt_DS5qZb2-Oc0p2C4eN_kyWq2fnvNsFZVE4D6qOMMUYSHiklChWC20IAUXpEZMcFQxUtUVrhRTQlOk60LFigeFi6TmKeV0Cu6PezvXfg_a93LXDq4JJyVJYk64EGkcKHSkStd673QtOxe-dKPESB6qlKFKeahSnqoMkbtjxGit_3HB4pSTlP4BcdJukg</recordid><startdate>202204</startdate><enddate>202204</enddate><creator>Katwe, Mayur</creator><creator>Singh, Keshav</creator><creator>Sharma, Prabhat Kumar</creator><creator>Li, Chih-Peng</creator><creator>Ding, Zhiguo</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>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-5280-384X</orcidid><orcidid>https://orcid.org/0000-0001-9028-4518</orcidid><orcidid>https://orcid.org/0000-0003-1548-3052</orcidid><orcidid>https://orcid.org/0000-0003-2906-9207</orcidid><orcidid>https://orcid.org/0000-0003-0050-0921</orcidid></search><sort><creationdate>202204</creationdate><title>Dynamic User Clustering and Optimal Power Allocation in UAV-Assisted Full-Duplex Hybrid NOMA System</title><author>Katwe, Mayur ; Singh, Keshav ; Sharma, Prabhat Kumar ; Li, Chih-Peng ; Ding, Zhiguo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-d851301994c239a5f9e92b892f05980d52dfd1da5a9e30efba4a8e301b6f87383</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Approximation</topic><topic>Cellular communication</topic><topic>Cluster analysis</topic><topic>Clustering</topic><topic>Cochannel interference</topic><topic>fullduplex (FD)</topic><topic>Hybrid systems</topic><topic>Interference</topic><topic>NOMA</topic><topic>non-orthogonal multiple access (NOMA)</topic><topic>Nonorthogonal multiple access</topic><topic>optimal UAV placement</topic><topic>Optimization</topic><topic>Placement</topic><topic>power allocation</topic><topic>Resource management</topic><topic>Search algorithms</topic><topic>Spectral efficiency</topic><topic>sum-rate maximization</topic><topic>Throughput</topic><topic>Unmanned aerial vehicles</topic><topic>Unmanned aerial vehicles (UAVs)</topic><topic>Vector quantization</topic><topic>Wireless communication</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Katwe, Mayur</creatorcontrib><creatorcontrib>Singh, Keshav</creatorcontrib><creatorcontrib>Sharma, Prabhat Kumar</creatorcontrib><creatorcontrib>Li, Chih-Peng</creatorcontrib><creatorcontrib>Ding, Zhiguo</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005–Present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE Xplore</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on wireless communications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Katwe, Mayur</au><au>Singh, Keshav</au><au>Sharma, Prabhat Kumar</au><au>Li, Chih-Peng</au><au>Ding, Zhiguo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dynamic User Clustering and Optimal Power Allocation in UAV-Assisted Full-Duplex Hybrid NOMA System</atitle><jtitle>IEEE transactions on wireless communications</jtitle><stitle>TWC</stitle><date>2022-04</date><risdate>2022</risdate><volume>21</volume><issue>4</issue><spage>2573</spage><epage>2590</epage><pages>2573-2590</pages><issn>1536-1276</issn><eissn>1558-2248</eissn><coden>ITWCAX</coden><abstract>This paper investigates unmanned aerial vehicles (UAVs)-assisted full-duplex (FD) non-orthogonal multiple access (NOMA) system based cellular network, aiming to improve overall sum-rate throughput of the system through dynamic user clustering, optimal UAV placement and power allocation. Since each UAV operates in FD mode, self-interference (SI), co-channel interference (CCI), inter-UAV interference (IUI) and intra-node interference (INI) dominate the system's performance. Consequently, we propose an unconventional two-stage dynamic user clustering for user nodes (UNs) to reduce the cross-interference in multi-UAV aided FD-NOMA system. Particularly, all UNs are initially clustered into <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula> clusters using k-means clustering in the first stage where each cluster is served by an UAV. Furthermore, each cluster is further divided into sub-clusters and each sub-clusters are operated in FD-NOMA scheme. Finally, to control interferences, a sum-rate throughput maximization problem is formulated for each UAV to jointly optimize uplink and downlink power allocation and UAV placement. The joint optimization problem is non-convex and difficult to solve directly, for which we decoupled the original problem by addressing UAV placement and power allocation separately. We first fix the UAV position and then solve the problem iteratively using successive convex approximation (SCA) method. By utilizing brute-force search algorithm, an optimal UAV placement is later performed which corresponds to maximum possible sum-rate throughput. Simulation results demonstrate that the proposed solution for the considered FD-NOMA system outperforms the conventional schemes.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TWC.2021.3113640</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0001-5280-384X</orcidid><orcidid>https://orcid.org/0000-0001-9028-4518</orcidid><orcidid>https://orcid.org/0000-0003-1548-3052</orcidid><orcidid>https://orcid.org/0000-0003-2906-9207</orcidid><orcidid>https://orcid.org/0000-0003-0050-0921</orcidid></addata></record> |
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subjects | Approximation Cellular communication Cluster analysis Clustering Cochannel interference fullduplex (FD) Hybrid systems Interference NOMA non-orthogonal multiple access (NOMA) Nonorthogonal multiple access optimal UAV placement Optimization Placement power allocation Resource management Search algorithms Spectral efficiency sum-rate maximization Throughput Unmanned aerial vehicles Unmanned aerial vehicles (UAVs) Vector quantization Wireless communication |
title | Dynamic User Clustering and Optimal Power Allocation in UAV-Assisted Full-Duplex Hybrid NOMA System |
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