Deployment-Friendly Link Adaptation in Wireless Local-Area Network Based on On-Line Reinforcement Learning
In this letter, based on outer loop link adaptation (OLLA), we propose an adaptive OLLA algorithm (AOLLA), which calculates the OLLA offset adapted to the current wireless channel environment in real-time by utilizing frequency statistics of packet error within the last observation window. AOLLA alg...
Gespeichert in:
| Veröffentlicht in: | IEEE communications letters 2023-12, Vol.27 (12), p.3424-3428 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 3428 |
|---|---|
| container_issue | 12 |
| container_start_page | 3424 |
| container_title | IEEE communications letters |
| container_volume | 27 |
| creator | Chen, Jie Ma, Juntao He, Yihao Wu, Gang |
| description | In this letter, based on outer loop link adaptation (OLLA), we propose an adaptive OLLA algorithm (AOLLA), which calculates the OLLA offset adapted to the current wireless channel environment in real-time by utilizing frequency statistics of packet error within the last observation window. AOLLA algorithm contains numerous parameters that require manual tuning, which raises the deployment difficulty of the algorithm. An online reinforcement learning algorithm is designed to tune the parameters automatically and allow the AOLLA algorithm to rapidly deploy in different environments. We worked out an experimental validation by deploying the proposed algorithm on a software-defined radio hardware platform in three typical scenarios for deploying a wireless local-area network using IEEE 802.11ax standard with its original HESU packet format. Experimental results show that our designed algorithm has a 59.6% performance improvement compared to the original OLLA. |
| doi_str_mv | 10.1109/LCOMM.2023.3327964 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_ieee_primary_10299668</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10299668</ieee_id><sourcerecordid>2901194671</sourcerecordid><originalsourceid>FETCH-LOGICAL-c296t-9e941f6043e7b7656fff8e3451658425ac18615caccb4559388ca1b1cd9261df3</originalsourceid><addsrcrecordid>eNpNkE1LAzEQhoMoWKt_QDwEPG_NxyabHGu1KmwtiOJxSbMTSbvN1mSL9N-7tT14mZnD-7wDD0LXlIwoJfqunMxnsxEjjI84Z4WW-QkaUCFUxvpx2t9E6awotDpHFyktCSGKCTpAywfYNO1uDaHLptFDqJsdLn1Y4XFtNp3pfBuwD_jTR2ggJVy21jTZOILBr9D9tHGF702CGve5ech6FPAb-ODaaGFfi0swMfjwdYnOnGkSXB33EH1MH98nz1k5f3qZjMvMMi27TIPOqZMk51AsCimkc04BzwWVQuVMGEuVpMIaaxe5EJorZQ1dUFtrJmnt-BDdHno3sf3eQuqqZbuNoX9ZMU0o1bksaJ9ih5SNbUoRXLWJfm3irqKk2jut_pxWe6fV0WkP3RwgDwD_AKa1lIr_AgIgcxI</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2901194671</pqid></control><display><type>article</type><title>Deployment-Friendly Link Adaptation in Wireless Local-Area Network Based on On-Line Reinforcement Learning</title><source>IEEE Electronic Library (IEL)</source><creator>Chen, Jie ; Ma, Juntao ; He, Yihao ; Wu, Gang</creator><creatorcontrib>Chen, Jie ; Ma, Juntao ; He, Yihao ; Wu, Gang</creatorcontrib><description>In this letter, based on outer loop link adaptation (OLLA), we propose an adaptive OLLA algorithm (AOLLA), which calculates the OLLA offset adapted to the current wireless channel environment in real-time by utilizing frequency statistics of packet error within the last observation window. AOLLA algorithm contains numerous parameters that require manual tuning, which raises the deployment difficulty of the algorithm. An online reinforcement learning algorithm is designed to tune the parameters automatically and allow the AOLLA algorithm to rapidly deploy in different environments. We worked out an experimental validation by deploying the proposed algorithm on a software-defined radio hardware platform in three typical scenarios for deploying a wireless local-area network using IEEE 802.11ax standard with its original HESU packet format. Experimental results show that our designed algorithm has a 59.6% performance improvement compared to the original OLLA.</description><identifier>ISSN: 1089-7798</identifier><identifier>EISSN: 1558-2558</identifier><identifier>DOI: 10.1109/LCOMM.2023.3327964</identifier><identifier>CODEN: ICLEF6</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Adaptation ; Adaptive algorithms ; Error analysis ; fading channel ; Fading channels ; Heuristic algorithms ; Local area networks ; Machine learning ; outer loop link adaptation ; Parameters ; Q-learning ; reinforcement learning ; Signal to noise ratio ; Software radio ; Stochastic processes ; Wireless communication ; Wireless networks</subject><ispartof>IEEE communications letters, 2023-12, Vol.27 (12), p.3424-3428</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c296t-9e941f6043e7b7656fff8e3451658425ac18615caccb4559388ca1b1cd9261df3</citedby><cites>FETCH-LOGICAL-c296t-9e941f6043e7b7656fff8e3451658425ac18615caccb4559388ca1b1cd9261df3</cites><orcidid>0000-0002-7367-3308 ; 0000-0002-3163-4132 ; 0000-0002-0394-2461 ; 0000-0001-9595-527X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10299668$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,782,786,798,27933,27934,54767</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10299668$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Chen, Jie</creatorcontrib><creatorcontrib>Ma, Juntao</creatorcontrib><creatorcontrib>He, Yihao</creatorcontrib><creatorcontrib>Wu, Gang</creatorcontrib><title>Deployment-Friendly Link Adaptation in Wireless Local-Area Network Based on On-Line Reinforcement Learning</title><title>IEEE communications letters</title><addtitle>LCOMM</addtitle><description>In this letter, based on outer loop link adaptation (OLLA), we propose an adaptive OLLA algorithm (AOLLA), which calculates the OLLA offset adapted to the current wireless channel environment in real-time by utilizing frequency statistics of packet error within the last observation window. AOLLA algorithm contains numerous parameters that require manual tuning, which raises the deployment difficulty of the algorithm. An online reinforcement learning algorithm is designed to tune the parameters automatically and allow the AOLLA algorithm to rapidly deploy in different environments. We worked out an experimental validation by deploying the proposed algorithm on a software-defined radio hardware platform in three typical scenarios for deploying a wireless local-area network using IEEE 802.11ax standard with its original HESU packet format. Experimental results show that our designed algorithm has a 59.6% performance improvement compared to the original OLLA.</description><subject>Adaptation</subject><subject>Adaptive algorithms</subject><subject>Error analysis</subject><subject>fading channel</subject><subject>Fading channels</subject><subject>Heuristic algorithms</subject><subject>Local area networks</subject><subject>Machine learning</subject><subject>outer loop link adaptation</subject><subject>Parameters</subject><subject>Q-learning</subject><subject>reinforcement learning</subject><subject>Signal to noise ratio</subject><subject>Software radio</subject><subject>Stochastic processes</subject><subject>Wireless communication</subject><subject>Wireless networks</subject><issn>1089-7798</issn><issn>1558-2558</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkE1LAzEQhoMoWKt_QDwEPG_NxyabHGu1KmwtiOJxSbMTSbvN1mSL9N-7tT14mZnD-7wDD0LXlIwoJfqunMxnsxEjjI84Z4WW-QkaUCFUxvpx2t9E6awotDpHFyktCSGKCTpAywfYNO1uDaHLptFDqJsdLn1Y4XFtNp3pfBuwD_jTR2ggJVy21jTZOILBr9D9tHGF702CGve5ech6FPAb-ODaaGFfi0swMfjwdYnOnGkSXB33EH1MH98nz1k5f3qZjMvMMi27TIPOqZMk51AsCimkc04BzwWVQuVMGEuVpMIaaxe5EJorZQ1dUFtrJmnt-BDdHno3sf3eQuqqZbuNoX9ZMU0o1bksaJ9ih5SNbUoRXLWJfm3irqKk2jut_pxWe6fV0WkP3RwgDwD_AKa1lIr_AgIgcxI</recordid><startdate>20231201</startdate><enddate>20231201</enddate><creator>Chen, Jie</creator><creator>Ma, Juntao</creator><creator>He, Yihao</creator><creator>Wu, Gang</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/0000-0002-7367-3308</orcidid><orcidid>https://orcid.org/0000-0002-3163-4132</orcidid><orcidid>https://orcid.org/0000-0002-0394-2461</orcidid><orcidid>https://orcid.org/0000-0001-9595-527X</orcidid></search><sort><creationdate>20231201</creationdate><title>Deployment-Friendly Link Adaptation in Wireless Local-Area Network Based on On-Line Reinforcement Learning</title><author>Chen, Jie ; Ma, Juntao ; He, Yihao ; Wu, Gang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c296t-9e941f6043e7b7656fff8e3451658425ac18615caccb4559388ca1b1cd9261df3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Adaptation</topic><topic>Adaptive algorithms</topic><topic>Error analysis</topic><topic>fading channel</topic><topic>Fading channels</topic><topic>Heuristic algorithms</topic><topic>Local area networks</topic><topic>Machine learning</topic><topic>outer loop link adaptation</topic><topic>Parameters</topic><topic>Q-learning</topic><topic>reinforcement learning</topic><topic>Signal to noise ratio</topic><topic>Software radio</topic><topic>Stochastic processes</topic><topic>Wireless communication</topic><topic>Wireless networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Jie</creatorcontrib><creatorcontrib>Ma, Juntao</creatorcontrib><creatorcontrib>He, Yihao</creatorcontrib><creatorcontrib>Wu, Gang</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>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE communications letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chen, Jie</au><au>Ma, Juntao</au><au>He, Yihao</au><au>Wu, Gang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deployment-Friendly Link Adaptation in Wireless Local-Area Network Based on On-Line Reinforcement Learning</atitle><jtitle>IEEE communications letters</jtitle><stitle>LCOMM</stitle><date>2023-12-01</date><risdate>2023</risdate><volume>27</volume><issue>12</issue><spage>3424</spage><epage>3428</epage><pages>3424-3428</pages><issn>1089-7798</issn><eissn>1558-2558</eissn><coden>ICLEF6</coden><abstract>In this letter, based on outer loop link adaptation (OLLA), we propose an adaptive OLLA algorithm (AOLLA), which calculates the OLLA offset adapted to the current wireless channel environment in real-time by utilizing frequency statistics of packet error within the last observation window. AOLLA algorithm contains numerous parameters that require manual tuning, which raises the deployment difficulty of the algorithm. An online reinforcement learning algorithm is designed to tune the parameters automatically and allow the AOLLA algorithm to rapidly deploy in different environments. We worked out an experimental validation by deploying the proposed algorithm on a software-defined radio hardware platform in three typical scenarios for deploying a wireless local-area network using IEEE 802.11ax standard with its original HESU packet format. Experimental results show that our designed algorithm has a 59.6% performance improvement compared to the original OLLA.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/LCOMM.2023.3327964</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0002-7367-3308</orcidid><orcidid>https://orcid.org/0000-0002-3163-4132</orcidid><orcidid>https://orcid.org/0000-0002-0394-2461</orcidid><orcidid>https://orcid.org/0000-0001-9595-527X</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1089-7798 |
| ispartof | IEEE communications letters, 2023-12, Vol.27 (12), p.3424-3428 |
| issn | 1089-7798 1558-2558 |
| language | eng |
| recordid | cdi_ieee_primary_10299668 |
| source | IEEE Electronic Library (IEL) |
| subjects | Adaptation Adaptive algorithms Error analysis fading channel Fading channels Heuristic algorithms Local area networks Machine learning outer loop link adaptation Parameters Q-learning reinforcement learning Signal to noise ratio Software radio Stochastic processes Wireless communication Wireless networks |
| title | Deployment-Friendly Link Adaptation in Wireless Local-Area Network Based on On-Line Reinforcement Learning |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-03T12%3A50%3A35IST&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=Deployment-Friendly%20Link%20Adaptation%20in%20Wireless%20Local-Area%20Network%20Based%20on%20On-Line%20Reinforcement%20Learning&rft.jtitle=IEEE%20communications%20letters&rft.au=Chen,%20Jie&rft.date=2023-12-01&rft.volume=27&rft.issue=12&rft.spage=3424&rft.epage=3428&rft.pages=3424-3428&rft.issn=1089-7798&rft.eissn=1558-2558&rft.coden=ICLEF6&rft_id=info:doi/10.1109/LCOMM.2023.3327964&rft_dat=%3Cproquest_RIE%3E2901194671%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=2901194671&rft_id=info:pmid/&rft_ieee_id=10299668&rfr_iscdi=true |