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Robust Cross-Layer Design with Reinforcement Learning for IEEE 802.11n Link Adaptation
This paper proposes a link adaptation method for IEEE 802.11n, which can foresightedly co-optimize the modulation and coding scheme (MCS) in the PHY layer and the frame size in the MAC layer. The link adaptation method employs Markov decision process (MDP) for modeling this crosslayer design. By sol...
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| creator | Kaijie Zhou |
| description | This paper proposes a link adaptation method for IEEE 802.11n, which can foresightedly co-optimize the modulation and coding scheme (MCS) in the PHY layer and the frame size in the MAC layer. The link adaptation method employs Markov decision process (MDP) for modeling this crosslayer design. By solving the MDP model with a reinforcement learning which does not require a prior knowledge about the wireless environment, the foresighted transmission strategy can be computed. The simulation results verify the proposed method and show that our proposed method can improve the goodput by 25% at most, compared with the MCS-oriented link adaptation method. |
| doi_str_mv | 10.1109/icc.2011.5963257 |
| format | conference_proceeding |
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The link adaptation method employs Markov decision process (MDP) for modeling this crosslayer design. By solving the MDP model with a reinforcement learning which does not require a prior knowledge about the wireless environment, the foresighted transmission strategy can be computed. The simulation results verify the proposed method and show that our proposed method can improve the goodput by 25% at most, compared with the MCS-oriented link adaptation method.</description><identifier>ISSN: 1550-3607</identifier><identifier>ISBN: 9781612842325</identifier><identifier>ISBN: 1612842321</identifier><identifier>EISSN: 1938-1883</identifier><identifier>EISBN: 161284233X</identifier><identifier>EISBN: 9781612842318</identifier><identifier>EISBN: 9781612842332</identifier><identifier>EISBN: 1612842313</identifier><identifier>DOI: 10.1109/icc.2011.5963257</identifier><language>eng</language><publisher>IEEE</publisher><subject>Adaptation models ; IEEE 802.11n Standard ; Learning ; Markov processes ; Signal to noise ratio ; Wireless communication</subject><ispartof>2011 IEEE International Conference on Communications (ICC), 2011, p.1-5</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5963257$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54555,54920,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5963257$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Kaijie Zhou</creatorcontrib><title>Robust Cross-Layer Design with Reinforcement Learning for IEEE 802.11n Link Adaptation</title><title>2011 IEEE International Conference on Communications (ICC)</title><addtitle>icc</addtitle><description>This paper proposes a link adaptation method for IEEE 802.11n, which can foresightedly co-optimize the modulation and coding scheme (MCS) in the PHY layer and the frame size in the MAC layer. The link adaptation method employs Markov decision process (MDP) for modeling this crosslayer design. By solving the MDP model with a reinforcement learning which does not require a prior knowledge about the wireless environment, the foresighted transmission strategy can be computed. The simulation results verify the proposed method and show that our proposed method can improve the goodput by 25% at most, compared with the MCS-oriented link adaptation method.</description><subject>Adaptation models</subject><subject>IEEE 802.11n Standard</subject><subject>Learning</subject><subject>Markov processes</subject><subject>Signal to noise ratio</subject><subject>Wireless communication</subject><issn>1550-3607</issn><issn>1938-1883</issn><isbn>9781612842325</isbn><isbn>1612842321</isbn><isbn>161284233X</isbn><isbn>9781612842318</isbn><isbn>9781612842332</isbn><isbn>1612842313</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo1kE1LAzEUReMX2NbuBTf5A1Pz8nyZZFnqqIUBoai4K2kmqVGbKZMR6b93QF1duAcuh8vYJYgZgDDX0bmZFAAzMgollUdsDAqkvpGIr8dsBAZ1AVrjCZuaUv8zSacDIxIFKlGes3HO70KQNAgj9rJqN1-554uuzbmo7cF3_NbnuE38O_ZvfOVjCm3n_M6nntfedimmLR8qvqyqimshB7nE65g--Lyx-972sU0X7CzYz-ynfzlhz3fV0-KhqB_vl4t5XUQoqS8MNnoDlihQqdE3JFxptQCLavDzpE0DhOBAUnAKQnDYCIuboKVSaAkn7Op3N3rv1_su7mx3WP_9gz88mVNV</recordid><startdate>201106</startdate><enddate>201106</enddate><creator>Kaijie Zhou</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201106</creationdate><title>Robust Cross-Layer Design with Reinforcement Learning for IEEE 802.11n Link Adaptation</title><author>Kaijie Zhou</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-93d8b1a55f5783ed50c7a801a36529e589d1531c125fc61ffc3d0a3bf82663a53</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Adaptation models</topic><topic>IEEE 802.11n Standard</topic><topic>Learning</topic><topic>Markov processes</topic><topic>Signal to noise ratio</topic><topic>Wireless communication</topic><toplevel>online_resources</toplevel><creatorcontrib>Kaijie Zhou</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEL</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kaijie Zhou</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Robust Cross-Layer Design with Reinforcement Learning for IEEE 802.11n Link Adaptation</atitle><btitle>2011 IEEE International Conference on Communications (ICC)</btitle><stitle>icc</stitle><date>2011-06</date><risdate>2011</risdate><spage>1</spage><epage>5</epage><pages>1-5</pages><issn>1550-3607</issn><eissn>1938-1883</eissn><isbn>9781612842325</isbn><isbn>1612842321</isbn><eisbn>161284233X</eisbn><eisbn>9781612842318</eisbn><eisbn>9781612842332</eisbn><eisbn>1612842313</eisbn><abstract>This paper proposes a link adaptation method for IEEE 802.11n, which can foresightedly co-optimize the modulation and coding scheme (MCS) in the PHY layer and the frame size in the MAC layer. The link adaptation method employs Markov decision process (MDP) for modeling this crosslayer design. By solving the MDP model with a reinforcement learning which does not require a prior knowledge about the wireless environment, the foresighted transmission strategy can be computed. The simulation results verify the proposed method and show that our proposed method can improve the goodput by 25% at most, compared with the MCS-oriented link adaptation method.</abstract><pub>IEEE</pub><doi>10.1109/icc.2011.5963257</doi><tpages>5</tpages></addata></record> |
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| identifier | ISSN: 1550-3607 |
| ispartof | 2011 IEEE International Conference on Communications (ICC), 2011, p.1-5 |
| issn | 1550-3607 1938-1883 |
| language | eng |
| recordid | cdi_ieee_primary_5963257 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Adaptation models IEEE 802.11n Standard Learning Markov processes Signal to noise ratio Wireless communication |
| title | Robust Cross-Layer Design with Reinforcement Learning for IEEE 802.11n Link Adaptation |
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