Reinforcement Learning for Energy-Efficient User Association in UAV-Assisted Cellular Networks
In unmanned aerial vehicle (UAV)-assisted communications, there are two significant challenges that need to be addressed-optimized UAV placement and energy-efficient user association. These challenges are crucial in meeting the quality-of-service requirements of users. To overcome these challenges,...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on aerospace and electronic systems 2024-04, Vol.60 (2), p.2474-2481 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 2481 |
|---|---|
| container_issue | 2 |
| container_start_page | 2474 |
| container_title | IEEE transactions on aerospace and electronic systems |
| container_volume | 60 |
| creator | Kaleem, Zeeshan Khalid, Waqas Ahmad, Ayaz Yu, Heejung Almasoud, Abdullah M. Yuen, Chau |
| description | In unmanned aerial vehicle (UAV)-assisted communications, there are two significant challenges that need to be addressed-optimized UAV placement and energy-efficient user association. These challenges are crucial in meeting the quality-of-service requirements of users. To overcome these challenges, a reinforcement-learning-based intelligent solution is proposed along with a reward function that associates users with UAVs in an intelligent manner. This solution aims to improve the system's sum rate performance by consuming less energy. Simulation results are presented to demonstrate the effectiveness of the proposed approach. The results indicate that the proposed approach is more energy efficient than the benchmark scheme while improving the system's sum rate. |
| doi_str_mv | 10.1109/TAES.2024.3353724 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_crossref_primary_10_1109_TAES_2024_3353724</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10409538</ieee_id><sourcerecordid>3037645594</sourcerecordid><originalsourceid>FETCH-LOGICAL-c246t-98bb9094db1003f8f115611af03b780a0f09e10dbc9b2621177a0fa017bb124f3</originalsourceid><addsrcrecordid>eNpNkM1Lw0AQxRdRsFb_AMFDwHPqzH4k2WMo9QOKgrYeDbvpbtmabupuivS_N6E9eBrmzXvz4EfILcIEEeTDopx9TChQPmFMsJzyMzJCIfJUZsDOyQgAi1RSgZfkKsZNv_KCsxH5ejfO2zbUZmt8l8yNCt75ddJLycybsD6kM2td7YbrMpqQlDG2tVOda33ifLIsP9NecrEzq2RqmmbfqJC8mu63Dd_xmlxY1URzc5pjsnycLabP6fzt6WVaztOa8qxLZaG1BMlXGgGYLSyiyBCVBabzAhRYkAZhpWupaUYR87zXFGCuNVJu2ZjcH__uQvuzN7GrNu0--L6yYsDyjAshee_Co6sObYzB2GoX3FaFQ4VQDRirAWM1YKxOGPvM3THjjDH__BykYAX7Axlebh0</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3037645594</pqid></control><display><type>article</type><title>Reinforcement Learning for Energy-Efficient User Association in UAV-Assisted Cellular Networks</title><source>IEEE Electronic Library (IEL)</source><creator>Kaleem, Zeeshan ; Khalid, Waqas ; Ahmad, Ayaz ; Yu, Heejung ; Almasoud, Abdullah M. ; Yuen, Chau</creator><creatorcontrib>Kaleem, Zeeshan ; Khalid, Waqas ; Ahmad, Ayaz ; Yu, Heejung ; Almasoud, Abdullah M. ; Yuen, Chau</creatorcontrib><description>In unmanned aerial vehicle (UAV)-assisted communications, there are two significant challenges that need to be addressed-optimized UAV placement and energy-efficient user association. These challenges are crucial in meeting the quality-of-service requirements of users. To overcome these challenges, a reinforcement-learning-based intelligent solution is proposed along with a reward function that associates users with UAVs in an intelligent manner. This solution aims to improve the system's sum rate performance by consuming less energy. Simulation results are presented to demonstrate the effectiveness of the proposed approach. The results indicate that the proposed approach is more energy efficient than the benchmark scheme while improving the system's sum rate.</description><identifier>ISSN: 0018-9251</identifier><identifier>EISSN: 1557-9603</identifier><identifier>DOI: 10.1109/TAES.2024.3353724</identifier><identifier>CODEN: IEARAX</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Autonomous aerial vehicles ; Base stations ; Cellular communication ; Cellular networks ; Energy efficiency ; Optimization ; Quality of service ; reinforcement learning (RL) ; Resource management ; Simulation ; Unmanned aerial vehicles ; unmanned aerial vehicles (UAVs) ; user association (UA) ; User requirements</subject><ispartof>IEEE transactions on aerospace and electronic systems, 2024-04, Vol.60 (2), p.2474-2481</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c246t-98bb9094db1003f8f115611af03b780a0f09e10dbc9b2621177a0fa017bb124f3</cites><orcidid>0000-0001-8046-2376 ; 0000-0001-8520-7219 ; 0000-0002-9805-970X ; 0000-0002-9307-2120 ; 0000-0002-7163-0443 ; 0000-0002-2253-6004</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10409538$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10409538$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Kaleem, Zeeshan</creatorcontrib><creatorcontrib>Khalid, Waqas</creatorcontrib><creatorcontrib>Ahmad, Ayaz</creatorcontrib><creatorcontrib>Yu, Heejung</creatorcontrib><creatorcontrib>Almasoud, Abdullah M.</creatorcontrib><creatorcontrib>Yuen, Chau</creatorcontrib><title>Reinforcement Learning for Energy-Efficient User Association in UAV-Assisted Cellular Networks</title><title>IEEE transactions on aerospace and electronic systems</title><addtitle>T-AES</addtitle><description>In unmanned aerial vehicle (UAV)-assisted communications, there are two significant challenges that need to be addressed-optimized UAV placement and energy-efficient user association. These challenges are crucial in meeting the quality-of-service requirements of users. To overcome these challenges, a reinforcement-learning-based intelligent solution is proposed along with a reward function that associates users with UAVs in an intelligent manner. This solution aims to improve the system's sum rate performance by consuming less energy. Simulation results are presented to demonstrate the effectiveness of the proposed approach. The results indicate that the proposed approach is more energy efficient than the benchmark scheme while improving the system's sum rate.</description><subject>Autonomous aerial vehicles</subject><subject>Base stations</subject><subject>Cellular communication</subject><subject>Cellular networks</subject><subject>Energy efficiency</subject><subject>Optimization</subject><subject>Quality of service</subject><subject>reinforcement learning (RL)</subject><subject>Resource management</subject><subject>Simulation</subject><subject>Unmanned aerial vehicles</subject><subject>unmanned aerial vehicles (UAVs)</subject><subject>user association (UA)</subject><subject>User requirements</subject><issn>0018-9251</issn><issn>1557-9603</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkM1Lw0AQxRdRsFb_AMFDwHPqzH4k2WMo9QOKgrYeDbvpbtmabupuivS_N6E9eBrmzXvz4EfILcIEEeTDopx9TChQPmFMsJzyMzJCIfJUZsDOyQgAi1RSgZfkKsZNv_KCsxH5ejfO2zbUZmt8l8yNCt75ddJLycybsD6kM2td7YbrMpqQlDG2tVOda33ifLIsP9NecrEzq2RqmmbfqJC8mu63Dd_xmlxY1URzc5pjsnycLabP6fzt6WVaztOa8qxLZaG1BMlXGgGYLSyiyBCVBabzAhRYkAZhpWupaUYR87zXFGCuNVJu2ZjcH__uQvuzN7GrNu0--L6yYsDyjAshee_Co6sObYzB2GoX3FaFQ4VQDRirAWM1YKxOGPvM3THjjDH__BykYAX7Axlebh0</recordid><startdate>20240401</startdate><enddate>20240401</enddate><creator>Kaleem, Zeeshan</creator><creator>Khalid, Waqas</creator><creator>Ahmad, Ayaz</creator><creator>Yu, Heejung</creator><creator>Almasoud, Abdullah M.</creator><creator>Yuen, Chau</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>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0001-8046-2376</orcidid><orcidid>https://orcid.org/0000-0001-8520-7219</orcidid><orcidid>https://orcid.org/0000-0002-9805-970X</orcidid><orcidid>https://orcid.org/0000-0002-9307-2120</orcidid><orcidid>https://orcid.org/0000-0002-7163-0443</orcidid><orcidid>https://orcid.org/0000-0002-2253-6004</orcidid></search><sort><creationdate>20240401</creationdate><title>Reinforcement Learning for Energy-Efficient User Association in UAV-Assisted Cellular Networks</title><author>Kaleem, Zeeshan ; Khalid, Waqas ; Ahmad, Ayaz ; Yu, Heejung ; Almasoud, Abdullah M. ; Yuen, Chau</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c246t-98bb9094db1003f8f115611af03b780a0f09e10dbc9b2621177a0fa017bb124f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Autonomous aerial vehicles</topic><topic>Base stations</topic><topic>Cellular communication</topic><topic>Cellular networks</topic><topic>Energy efficiency</topic><topic>Optimization</topic><topic>Quality of service</topic><topic>reinforcement learning (RL)</topic><topic>Resource management</topic><topic>Simulation</topic><topic>Unmanned aerial vehicles</topic><topic>unmanned aerial vehicles (UAVs)</topic><topic>user association (UA)</topic><topic>User requirements</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kaleem, Zeeshan</creatorcontrib><creatorcontrib>Khalid, Waqas</creatorcontrib><creatorcontrib>Ahmad, Ayaz</creatorcontrib><creatorcontrib>Yu, Heejung</creatorcontrib><creatorcontrib>Almasoud, Abdullah M.</creatorcontrib><creatorcontrib>Yuen, Chau</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><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on aerospace and electronic systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kaleem, Zeeshan</au><au>Khalid, Waqas</au><au>Ahmad, Ayaz</au><au>Yu, Heejung</au><au>Almasoud, Abdullah M.</au><au>Yuen, Chau</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reinforcement Learning for Energy-Efficient User Association in UAV-Assisted Cellular Networks</atitle><jtitle>IEEE transactions on aerospace and electronic systems</jtitle><stitle>T-AES</stitle><date>2024-04-01</date><risdate>2024</risdate><volume>60</volume><issue>2</issue><spage>2474</spage><epage>2481</epage><pages>2474-2481</pages><issn>0018-9251</issn><eissn>1557-9603</eissn><coden>IEARAX</coden><abstract>In unmanned aerial vehicle (UAV)-assisted communications, there are two significant challenges that need to be addressed-optimized UAV placement and energy-efficient user association. These challenges are crucial in meeting the quality-of-service requirements of users. To overcome these challenges, a reinforcement-learning-based intelligent solution is proposed along with a reward function that associates users with UAVs in an intelligent manner. This solution aims to improve the system's sum rate performance by consuming less energy. Simulation results are presented to demonstrate the effectiveness of the proposed approach. The results indicate that the proposed approach is more energy efficient than the benchmark scheme while improving the system's sum rate.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TAES.2024.3353724</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0001-8046-2376</orcidid><orcidid>https://orcid.org/0000-0001-8520-7219</orcidid><orcidid>https://orcid.org/0000-0002-9805-970X</orcidid><orcidid>https://orcid.org/0000-0002-9307-2120</orcidid><orcidid>https://orcid.org/0000-0002-7163-0443</orcidid><orcidid>https://orcid.org/0000-0002-2253-6004</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 0018-9251 |
| ispartof | IEEE transactions on aerospace and electronic systems, 2024-04, Vol.60 (2), p.2474-2481 |
| issn | 0018-9251 1557-9603 |
| language | eng |
| recordid | cdi_crossref_primary_10_1109_TAES_2024_3353724 |
| source | IEEE Electronic Library (IEL) |
| subjects | Autonomous aerial vehicles Base stations Cellular communication Cellular networks Energy efficiency Optimization Quality of service reinforcement learning (RL) Resource management Simulation Unmanned aerial vehicles unmanned aerial vehicles (UAVs) user association (UA) User requirements |
| title | Reinforcement Learning for Energy-Efficient User Association in UAV-Assisted Cellular Networks |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-26T23%3A40%3A05IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Reinforcement%20Learning%20for%20Energy-Efficient%20User%20Association%20in%20UAV-Assisted%20Cellular%20Networks&rft.jtitle=IEEE%20transactions%20on%20aerospace%20and%20electronic%20systems&rft.au=Kaleem,%20Zeeshan&rft.date=2024-04-01&rft.volume=60&rft.issue=2&rft.spage=2474&rft.epage=2481&rft.pages=2474-2481&rft.issn=0018-9251&rft.eissn=1557-9603&rft.coden=IEARAX&rft_id=info:doi/10.1109/TAES.2024.3353724&rft_dat=%3Cproquest_RIE%3E3037645594%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3037645594&rft_id=info:pmid/&rft_ieee_id=10409538&rfr_iscdi=true |