Learning to Be Energy-Efficient in Cooperative Networks
Cooperative communication has great potential to improve the transmit diversity in multiple users environments. To achieve a high network-wide energy-efficient performance, this letter poses the relay selection problem of cooperative communication as a noncooperative automata game considering nodes&...
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| Veröffentlicht in: | IEEE communications letters 2016-12, Vol.20 (12), p.2518-2521 |
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| container_title | IEEE communications letters |
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| creator | Tian, Daxin Zhou, Jianshan Sheng, Zhengguo Ni, Qiang |
| description | Cooperative communication has great potential to improve the transmit diversity in multiple users environments. To achieve a high network-wide energy-efficient performance, this letter poses the relay selection problem of cooperative communication as a noncooperative automata game considering nodes' selfishness, proving that it is an ordinal game (OPG), and presents a game-theoretic analysis to address the benefit-equilibrium decision-making issue in relay selection. A stochastic learning-based relay selection algorithm is proposed for transmitters to learn a Nash-equilibrium strategy in a distributed manner. We prove through the theoretical and numerical analysis that the proposed algorithm is guaranteed to converge to a Nash equilibrium state, where the resulting cooperative network is energy efficient and reliable. The strength of the proposed algorithm is also confirmed through comparative simulations in terms of energy benefit and fairness performances. |
| doi_str_mv | 10.1109/LCOMM.2016.2608820 |
| format | Article |
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To achieve a high network-wide energy-efficient performance, this letter poses the relay selection problem of cooperative communication as a noncooperative automata game considering nodes' selfishness, proving that it is an ordinal game (OPG), and presents a game-theoretic analysis to address the benefit-equilibrium decision-making issue in relay selection. A stochastic learning-based relay selection algorithm is proposed for transmitters to learn a Nash-equilibrium strategy in a distributed manner. We prove through the theoretical and numerical analysis that the proposed algorithm is guaranteed to converge to a Nash equilibrium state, where the resulting cooperative network is energy efficient and reliable. The strength of the proposed algorithm is also confirmed through comparative simulations in terms of energy benefit and fairness performances.</description><identifier>ISSN: 1089-7798</identifier><identifier>EISSN: 1558-2558</identifier><identifier>DOI: 10.1109/LCOMM.2016.2608820</identifier><identifier>CODEN: ICLEF6</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithm design and analysis ; Algorithms ; Computer simulation ; Cooperative communication ; Cooperative networks ; Cooperative systems ; decentralized learning ; Decision analysis ; Decision making ; Decision theory ; Energy transmission ; energy-efficiency ; Equilibrium ; Game theory ; Machine learning ; Numerical analysis ; Relay ; Relays ; self-organized relay selection ; Transmitters</subject><ispartof>IEEE communications letters, 2016-12, Vol.20 (12), p.2518-2521</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c409t-7ad8ef202f3c7732a05eec9026090ba23d6f91e7c91d88976088351a285e64523</citedby><cites>FETCH-LOGICAL-c409t-7ad8ef202f3c7732a05eec9026090ba23d6f91e7c91d88976088351a285e64523</cites><orcidid>0000-0002-4593-1656 ; 0000-0001-7796-5650</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7565480$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27923,27924,54757</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7565480$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Tian, Daxin</creatorcontrib><creatorcontrib>Zhou, Jianshan</creatorcontrib><creatorcontrib>Sheng, Zhengguo</creatorcontrib><creatorcontrib>Ni, Qiang</creatorcontrib><title>Learning to Be Energy-Efficient in Cooperative Networks</title><title>IEEE communications letters</title><addtitle>COML</addtitle><description>Cooperative communication has great potential to improve the transmit diversity in multiple users environments. To achieve a high network-wide energy-efficient performance, this letter poses the relay selection problem of cooperative communication as a noncooperative automata game considering nodes' selfishness, proving that it is an ordinal game (OPG), and presents a game-theoretic analysis to address the benefit-equilibrium decision-making issue in relay selection. A stochastic learning-based relay selection algorithm is proposed for transmitters to learn a Nash-equilibrium strategy in a distributed manner. We prove through the theoretical and numerical analysis that the proposed algorithm is guaranteed to converge to a Nash equilibrium state, where the resulting cooperative network is energy efficient and reliable. The strength of the proposed algorithm is also confirmed through comparative simulations in terms of energy benefit and fairness performances.</description><subject>Algorithm design and analysis</subject><subject>Algorithms</subject><subject>Computer simulation</subject><subject>Cooperative communication</subject><subject>Cooperative networks</subject><subject>Cooperative systems</subject><subject>decentralized learning</subject><subject>Decision analysis</subject><subject>Decision making</subject><subject>Decision theory</subject><subject>Energy transmission</subject><subject>energy-efficiency</subject><subject>Equilibrium</subject><subject>Game theory</subject><subject>Machine learning</subject><subject>Numerical analysis</subject><subject>Relay</subject><subject>Relays</subject><subject>self-organized relay selection</subject><subject>Transmitters</subject><issn>1089-7798</issn><issn>1558-2558</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kEtPwzAQhC0EEqXwB-ASiXPK2olfR4jKQ0rpBc6WSdeVC8TBTkH99yS04rKzh5nd0UfIJYUZpaBv6mq5WMwYUDFjApRicEQmlHOVs2EcDzsonUup1Sk5S2kDAIpxOiGyRhtb366zPmR3mM1bjOtdPnfONx7bPvNtVoXQYbS9_8bsGfufEN_TOTlx9iPhxUGn5PV-_lI95vXy4am6rfOmBN3n0q4UOgbMFY2UBbPAERsNQ0kNb5YVK-E0RdloulJKy7F7walliqMoOSum5Hp_t4vha4upN5uwje3w0lBVKiYF52Jwsb2riSGliM500X_auDMUzAjI_AEyIyBzADSErvYhj4j_AckFLxUUv1dBX5Q</recordid><startdate>20161201</startdate><enddate>20161201</enddate><creator>Tian, Daxin</creator><creator>Zhou, Jianshan</creator><creator>Sheng, Zhengguo</creator><creator>Ni, Qiang</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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To achieve a high network-wide energy-efficient performance, this letter poses the relay selection problem of cooperative communication as a noncooperative automata game considering nodes' selfishness, proving that it is an ordinal game (OPG), and presents a game-theoretic analysis to address the benefit-equilibrium decision-making issue in relay selection. A stochastic learning-based relay selection algorithm is proposed for transmitters to learn a Nash-equilibrium strategy in a distributed manner. We prove through the theoretical and numerical analysis that the proposed algorithm is guaranteed to converge to a Nash equilibrium state, where the resulting cooperative network is energy efficient and reliable. 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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithm design and analysis Algorithms Computer simulation Cooperative communication Cooperative networks Cooperative systems decentralized learning Decision analysis Decision making Decision theory Energy transmission energy-efficiency Equilibrium Game theory Machine learning Numerical analysis Relay Relays self-organized relay selection Transmitters |
| title | Learning to Be Energy-Efficient in Cooperative Networks |
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