{"id":"https://openalex.org/W3190857346","doi":"https://doi.org/10.1109/iwcmc51323.2021.9498749","title":"Graph convolution network deep reinforcement learning approach based on manifold regularization in cognitive radio network","display_name":"Graph convolution network deep reinforcement learning approach based on manifold regularization in cognitive radio network","publication_year":2021,"publication_date":"2021-06-28","ids":{"openalex":"https://openalex.org/W3190857346","doi":"https://doi.org/10.1109/iwcmc51323.2021.9498749","mag":"3190857346"},"language":"en","primary_location":{"id":"doi:10.1109/iwcmc51323.2021.9498749","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc51323.2021.9498749","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Wireless Communications and Mobile Computing (IWCMC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100783121","display_name":"Yanyan Zhang","orcid":"https://orcid.org/0000-0001-6118-7604"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhang Yanyan","raw_affiliation_strings":["5G+ Vertical Service Innovation China Mobile Group Design Institute Co., Ltd., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"5G+ Vertical Service Innovation China Mobile Group Design Institute Co., Ltd., Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100376632","display_name":"Zeyu Liu","orcid":"https://orcid.org/0000-0001-7291-3106"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liu Zeyu","raw_affiliation_strings":["School of Information and Communication Engineering Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010697502","display_name":"Baocong Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wang Baocong","raw_affiliation_strings":["5G+ Vertical Service Innovation China Mobile Group Design Institute Co., Ltd., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"5G+ Vertical Service Innovation China Mobile Group Design Institute Co., Ltd., Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"2018","issue":null,"first_page":"1275","last_page":"1280"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10579","display_name":"Cognitive Radio Networks and Spectrum Sensing","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cognitive-radio","display_name":"Cognitive radio","score":0.8161343336105347},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.782311201095581},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7179057598114014},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6049328446388245},{"id":"https://openalex.org/keywords/cognitive-network","display_name":"Cognitive network","score":0.5081915855407715},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49776697158813477},{"id":"https://openalex.org/keywords/q-learning","display_name":"Q-learning","score":0.4832768738269806},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4167883098125458},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3900512456893921},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3850690722465515},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.26339471340179443},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.1383305788040161},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.13397949934005737},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1336899697780609}],"concepts":[{"id":"https://openalex.org/C149946192","wikidata":"https://www.wikidata.org/wiki/Q3235733","display_name":"Cognitive radio","level":3,"score":0.8161343336105347},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.782311201095581},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7179057598114014},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6049328446388245},{"id":"https://openalex.org/C32542511","wikidata":"https://www.wikidata.org/wiki/Q5141225","display_name":"Cognitive network","level":4,"score":0.5081915855407715},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49776697158813477},{"id":"https://openalex.org/C188116033","wikidata":"https://www.wikidata.org/wiki/Q2664563","display_name":"Q-learning","level":3,"score":0.4832768738269806},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4167883098125458},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3900512456893921},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3850690722465515},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.26339471340179443},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.1383305788040161},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.13397949934005737},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1336899697780609}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwcmc51323.2021.9498749","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc51323.2021.9498749","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Wireless Communications and Mobile Computing (IWCMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2053186076","https://openalex.org/W2101262627","https://openalex.org/W2111072639","https://openalex.org/W2120144288","https://openalex.org/W2143304292","https://openalex.org/W2384446853","https://openalex.org/W2788499634","https://openalex.org/W2889980946","https://openalex.org/W2898035736","https://openalex.org/W2990045899","https://openalex.org/W3010396974","https://openalex.org/W3017967922","https://openalex.org/W3035649237","https://openalex.org/W3085842779","https://openalex.org/W6674928982","https://openalex.org/W6681608844"],"related_works":["https://openalex.org/W2277261328","https://openalex.org/W2610471112","https://openalex.org/W2167656028","https://openalex.org/W2340059876","https://openalex.org/W4376605461","https://openalex.org/W2357975469","https://openalex.org/W3096874164","https://openalex.org/W2136202932","https://openalex.org/W2010464513","https://openalex.org/W3087814763"],"abstract_inverted_index":{"In":[0],"this":[1,19],"paper,":[2],"we":[3],"propose":[4],"GCNMR-ELM":[5,79,95],"policy":[6,20,29,57,63,80,86,108,119],"model":[7,21,30,58,64,81,87,109,127],"for":[8],"deep":[9],"reinforcement":[10],"learning":[11],"approach":[12],"in":[13,22,51,70,88,98,110],"cognitive":[14,23],"radio":[15,24],"network":[16,72],"and":[17,37,55,94,134],"applications":[18],"on":[25,105],"steelworks":[26],"scene.":[27],"This":[28],"combines":[31],"the":[32,45,76,89,114,117,131],"advantage":[33],"of":[34,48,53,92,116],"GCNMR":[35],"framework":[36],"ELM":[38],"algorithm.":[39],"The":[40,61],"aim":[41],"is":[42],"to":[43,112],"enhance":[44],"data":[46,68,137],"rate":[47,78],"spectrum":[49],"sharing":[50],"cognitive?radio":[52],"steelworks,":[54],"Reduced":[56],"training":[59,132],"time.":[60],"proposed":[62,118],"has":[65],"a":[66],"higher":[67,136],"rates":[69],"CR":[71],"can":[73,128],"be":[74],"provided;":[75],"convergence":[77],"are":[82],"faster":[83],"than":[84],"other":[85],"same":[90],"number":[91],"iterations":[93],"no":[96],"increase":[97],"algorithm":[99],"complexity.":[100],"We":[101],"provides":[102],"extensive":[103],"experiments":[104],"three":[106],"different":[107],"order":[111],"evaluate":[113],"performance":[115],"model.":[120],"Experimental":[121],"results":[122],"show":[123],"that":[124],"our":[125],"strategy":[126],"effectively":[129],"reduce":[130],"time":[133],"provide":[135],"rate.":[138]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}