An Energy-Efficiency Multi-Relay Selection and Power Allocation Based on Deep Neural Network for Amplify-and-Forward Cooperative Transmission
In this letter, an energy-efficiency (EE) resource allocation strategy is investigated for the amplify-and-forward (AF) protocol used to forward data to the destination. First, we formulate an EE optimization problem of multi-relay AF system, which correlates the power of source with the power of ea...
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| Veröffentlicht in: | IEEE wireless communications letters 2022-01, Vol.11 (1), p.63-66 |
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| creator | Guo, Yan-Yan Yang, Jing Tan, Xiao-Long Liu, Qian |
| description | In this letter, an energy-efficiency (EE) resource allocation strategy is investigated for the amplify-and-forward (AF) protocol used to forward data to the destination. First, we formulate an EE optimization problem of multi-relay AF system, which correlates the power of source with the power of each relay. Then, a deep neural network (DNN)-based model is constructed to implement the joint power allocation of source and multiple relays. In particular, we adopt the modified rectified linear unit (ReLU) as the activation function in the output layer of the DNN model to hinder relays' negative output power weightings, thereby to simultaneously realize the selection of relays and their power allocation. The simulation results show that our scheme enables better performance in term of the EE of system compared with the conventional strategies and convolutional neural network (CNN). |
| doi_str_mv | 10.1109/LWC.2021.3120287 |
| format | Article |
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First, we formulate an EE optimization problem of multi-relay AF system, which correlates the power of source with the power of each relay. Then, a deep neural network (DNN)-based model is constructed to implement the joint power allocation of source and multiple relays. In particular, we adopt the modified rectified linear unit (ReLU) as the activation function in the output layer of the DNN model to hinder relays' negative output power weightings, thereby to simultaneously realize the selection of relays and their power allocation. The simulation results show that our scheme enables better performance in term of the EE of system compared with the conventional strategies and convolutional neural network (CNN).</description><identifier>ISSN: 2162-2337</identifier><identifier>EISSN: 2162-2345</identifier><identifier>DOI: 10.1109/LWC.2021.3120287</identifier><identifier>CODEN: IWCLAF</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>AF cooperation ; Amplification ; Artificial neural networks ; Deep learning ; energy-efficiency transmission ; Neural networks ; Optimization ; power allocation ; Protocols ; Reactive power ; Relay ; relay selection ; Relays ; Resource allocation ; Resource management ; Training</subject><ispartof>IEEE wireless communications letters, 2022-01, Vol.11 (1), p.63-66</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-d34a5747aebe76c54100e0cbcb0a66da0eefe0ab3cb1e985ff710466e77cd9af3</citedby><cites>FETCH-LOGICAL-c291t-d34a5747aebe76c54100e0cbcb0a66da0eefe0ab3cb1e985ff710466e77cd9af3</cites><orcidid>0000-0002-1610-6614 ; 0000-0001-7442-7101 ; 0000-0003-2868-0063 ; 0000-0002-8107-6132</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9570822$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9570822$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Guo, Yan-Yan</creatorcontrib><creatorcontrib>Yang, Jing</creatorcontrib><creatorcontrib>Tan, Xiao-Long</creatorcontrib><creatorcontrib>Liu, Qian</creatorcontrib><title>An Energy-Efficiency Multi-Relay Selection and Power Allocation Based on Deep Neural Network for Amplify-and-Forward Cooperative Transmission</title><title>IEEE wireless communications letters</title><addtitle>LWC</addtitle><description>In this letter, an energy-efficiency (EE) resource allocation strategy is investigated for the amplify-and-forward (AF) protocol used to forward data to the destination. 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The simulation results show that our scheme enables better performance in term of the EE of system compared with the conventional strategies and convolutional neural network (CNN).</description><subject>AF cooperation</subject><subject>Amplification</subject><subject>Artificial neural networks</subject><subject>Deep learning</subject><subject>energy-efficiency transmission</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>power allocation</subject><subject>Protocols</subject><subject>Reactive power</subject><subject>Relay</subject><subject>relay selection</subject><subject>Relays</subject><subject>Resource allocation</subject><subject>Resource management</subject><subject>Training</subject><issn>2162-2337</issn><issn>2162-2345</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kF9LwzAUxYsoOObeBV8CPncmaZu0j7NuKsw_6MTHkqY30pk1NWkd_RB-ZzMnuy_ncvmdc-EEwTnBU0JwdrV8z6cUUzKNiJeUHwUjShgNaRQnx4c94qfBxLk19sMwoSQdBT-zBs0bsB9DOFeqljU0ckAPve7q8AW0GNAraJBdbRokmgo9my1YNNPaSPF3vBYOKuSXG4AWPUJvhfbSbY39RMp4dtPqWg2hd4cLY7fCVig3pgXrA74Braxo3KZ2zqedBSdKaAeTfx0Hb4v5Kr8Ll0-39_lsGUqakS6solgkPOYCSuBMJjHBGLAsZYkFY5XAAAqwKCNZEsjSRClOcMwYcC6rTKhoHFzuc1trvnpwXbE2vW38y4IywuMsITjyFN5T0hrnLKiitfVG2KEguNj1Xvjei13vxX_v3nKxt9QAcMCzhOOU0ugXhduArw</recordid><startdate>202201</startdate><enddate>202201</enddate><creator>Guo, Yan-Yan</creator><creator>Yang, Jing</creator><creator>Tan, Xiao-Long</creator><creator>Liu, Qian</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/0000-0002-1610-6614</orcidid><orcidid>https://orcid.org/0000-0001-7442-7101</orcidid><orcidid>https://orcid.org/0000-0003-2868-0063</orcidid><orcidid>https://orcid.org/0000-0002-8107-6132</orcidid></search><sort><creationdate>202201</creationdate><title>An Energy-Efficiency Multi-Relay Selection and Power Allocation Based on Deep Neural Network for Amplify-and-Forward Cooperative Transmission</title><author>Guo, Yan-Yan ; Yang, Jing ; Tan, Xiao-Long ; Liu, Qian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-d34a5747aebe76c54100e0cbcb0a66da0eefe0ab3cb1e985ff710466e77cd9af3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>AF cooperation</topic><topic>Amplification</topic><topic>Artificial neural networks</topic><topic>Deep learning</topic><topic>energy-efficiency transmission</topic><topic>Neural networks</topic><topic>Optimization</topic><topic>power allocation</topic><topic>Protocols</topic><topic>Reactive power</topic><topic>Relay</topic><topic>relay selection</topic><topic>Relays</topic><topic>Resource allocation</topic><topic>Resource management</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Guo, Yan-Yan</creatorcontrib><creatorcontrib>Yang, Jing</creatorcontrib><creatorcontrib>Tan, Xiao-Long</creatorcontrib><creatorcontrib>Liu, Qian</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 Online</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE wireless communications letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Guo, Yan-Yan</au><au>Yang, Jing</au><au>Tan, Xiao-Long</au><au>Liu, Qian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Energy-Efficiency Multi-Relay Selection and Power Allocation Based on Deep Neural Network for Amplify-and-Forward Cooperative Transmission</atitle><jtitle>IEEE wireless communications letters</jtitle><stitle>LWC</stitle><date>2022-01</date><risdate>2022</risdate><volume>11</volume><issue>1</issue><spage>63</spage><epage>66</epage><pages>63-66</pages><issn>2162-2337</issn><eissn>2162-2345</eissn><coden>IWCLAF</coden><abstract>In this letter, an energy-efficiency (EE) resource allocation strategy is investigated for the amplify-and-forward (AF) protocol used to forward data to the destination. First, we formulate an EE optimization problem of multi-relay AF system, which correlates the power of source with the power of each relay. Then, a deep neural network (DNN)-based model is constructed to implement the joint power allocation of source and multiple relays. In particular, we adopt the modified rectified linear unit (ReLU) as the activation function in the output layer of the DNN model to hinder relays' negative output power weightings, thereby to simultaneously realize the selection of relays and their power allocation. The simulation results show that our scheme enables better performance in term of the EE of system compared with the conventional strategies and convolutional neural network (CNN).</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/LWC.2021.3120287</doi><tpages>4</tpages><orcidid>https://orcid.org/0000-0002-1610-6614</orcidid><orcidid>https://orcid.org/0000-0001-7442-7101</orcidid><orcidid>https://orcid.org/0000-0003-2868-0063</orcidid><orcidid>https://orcid.org/0000-0002-8107-6132</orcidid></addata></record> |
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| subjects | AF cooperation Amplification Artificial neural networks Deep learning energy-efficiency transmission Neural networks Optimization power allocation Protocols Reactive power Relay relay selection Relays Resource allocation Resource management Training |
| title | An Energy-Efficiency Multi-Relay Selection and Power Allocation Based on Deep Neural Network for Amplify-and-Forward Cooperative Transmission |
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