{"id":"https://openalex.org/W4408324697","doi":"https://doi.org/10.1109/globecom52923.2024.10901762","title":"Reinforcement Learning Based Age of Information Minimization for Downlink NOMA Systems","display_name":"Reinforcement Learning Based Age of Information Minimization for Downlink NOMA Systems","publication_year":2024,"publication_date":"2024-12-08","ids":{"openalex":"https://openalex.org/W4408324697","doi":"https://doi.org/10.1109/globecom52923.2024.10901762"},"language":"en","primary_location":{"id":"doi:10.1109/globecom52923.2024.10901762","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom52923.2024.10901762","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2024 - 2024 IEEE Global Communications Conference","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/A5004464411","display_name":"Waleed Ahsan","orcid":"https://orcid.org/0000-0002-3550-7077"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Waleed Ahsan","raw_affiliation_strings":["University of Edinburgh,Edinburgh,UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Edinburgh,Edinburgh,UK","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Anees Ahsan","orcid":null},"institutions":[{"id":"https://openalex.org/I184856343","display_name":"Wrexham University","ror":"https://ror.org/048kc0s52","country_code":"GB","type":"education","lineage":["https://openalex.org/I184856343"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Anees Ahsan","raw_affiliation_strings":["Glyndwr University,UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Glyndwr University,UK","institution_ids":["https://openalex.org/I184856343"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5116585525","display_name":"Faiqa Bibi","orcid":null},"institutions":[{"id":"https://openalex.org/I79571142","display_name":"Virtual University of Pakistan","ror":"https://ror.org/00ya1zd25","country_code":"PK","type":"education","lineage":["https://openalex.org/I79571142"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Faiqa Bibi","raw_affiliation_strings":["Virtual University,Islamabad,Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Virtual University,Islamabad,Pakistan","institution_ids":["https://openalex.org/I79571142"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3322","last_page":"3327"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13553","display_name":"Age of Information Optimization","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T13553","display_name":"Age of Information Optimization","score":0.9998000264167786,"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"}},{"id":"https://openalex.org/T12079","display_name":"IoT Networks and Protocols","score":0.9700999855995178,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13471","display_name":"Cognitive Functions and Memory","score":0.9264000058174133,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/noma","display_name":"Noma","score":0.964339017868042},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8112999200820923},{"id":"https://openalex.org/keywords/telecommunications-link","display_name":"Telecommunications link","score":0.7565701007843018},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7022834420204163},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.6403127908706665},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33580929040908813},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.22088995575904846},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.07464751601219177}],"concepts":[{"id":"https://openalex.org/C2775918612","wikidata":"https://www.wikidata.org/wiki/Q994794","display_name":"Noma","level":3,"score":0.964339017868042},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8112999200820923},{"id":"https://openalex.org/C138660444","wikidata":"https://www.wikidata.org/wiki/Q5607897","display_name":"Telecommunications link","level":2,"score":0.7565701007843018},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7022834420204163},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.6403127908706665},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33580929040908813},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.22088995575904846},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.07464751601219177}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom52923.2024.10901762","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom52923.2024.10901762","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2024 - 2024 IEEE Global Communications Conference","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":13,"referenced_works":["https://openalex.org/W1993918491","https://openalex.org/W2791487310","https://openalex.org/W2917981112","https://openalex.org/W2969525674","https://openalex.org/W3020925646","https://openalex.org/W3039041378","https://openalex.org/W3081560128","https://openalex.org/W3084578038","https://openalex.org/W3134554244","https://openalex.org/W3137257456","https://openalex.org/W3163822322","https://openalex.org/W3198713257","https://openalex.org/W4386917299"],"related_works":["https://openalex.org/W3030525848","https://openalex.org/W4385197910","https://openalex.org/W2340639785","https://openalex.org/W4404253608","https://openalex.org/W3010814398","https://openalex.org/W4385197899","https://openalex.org/W3005957545","https://openalex.org/W3165611029","https://openalex.org/W2370038111","https://openalex.org/W2950413682"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,29,49],"develop":[4],"learning":[5,170],"based":[6,15,94,111],"strategies":[7],"to":[8,64,123,148],"optimize":[9],"the":[10,76,79,82,98,113,125,128,188,195],"age":[11],"of":[12,78,127,158,187],"information":[13],"(AoI)":[14],"resource":[16,87],"allocation":[17,152],"for":[18,34,95,101,154,172],"downlink":[19],"non-orthogonal":[20],"multiple":[21,198],"access":[22,199],"(NOMA)":[23],"systems.":[24],"Considering":[25],"dynamic":[26,86],"network":[27,159],"loads,":[28],"design":[30,50],"state-action-reward-state-action":[31],"(SARSA)":[32],"Q-learning":[33],"small-scale":[35],"networks,":[36,48],"as":[37,60],"it":[38],"provides":[39],"more":[40],"reliable":[41],"policy":[42,54],"than":[43,194],"traditional":[44],"Q-learning.":[45],"For":[46],"large-scale":[47],"a":[51,107],"deep":[52,71],"deterministic":[53],"gradient":[55],"(DDPG)":[56],"cooperative":[57],"agent":[58],"system,":[59],"DDPG":[61,132],"is":[62,192],"able":[63,147],"efficiently":[65],"handle":[66],"huge":[67],"state-action":[68],"spaces":[69],"with":[70],"neural":[72],"networks":[73],"(DNNs).":[74],"With":[75],"aid":[77],"designed":[80],"algorithms,":[81],"entire":[83],"AoI":[84,190,200],"centric":[85],"allocations":[88],"are":[89,146],"updated":[90],"using":[91],"actor-":[92],"critic":[93],"DNNs":[96,129],"and":[97,118,133,180],"SARSA":[99],"Q-table":[100,134],"large/small":[102],"scale":[103],"networks.":[104],"We":[105],"propose":[106],"multi-objective":[108,164],"reward":[109,165],"system":[110,191],"on":[112],"sum":[114],"rate,":[115],"mean":[116],"AoI,":[117],"max":[119],"power,":[120],"which":[121],"helps":[122],"improve":[124],"update":[126],"weights":[130],"in":[131,136],"values":[135],"SARSA.":[137],"The":[138,162,182],"simulation":[139],"outcomes":[140],"prove":[141],"that":[142],"a)":[143],"Proposed":[144],"schemes":[145],"find":[149],"efficient":[150],"optimal":[151],"policies":[153],"all":[155],"considered":[156],"types":[157],"traffic;":[160],"b)":[161],"proposed":[163],"function":[166],"can":[167],"assist":[168],"reinforcement":[169],"agents":[171],"long-term":[173,183],"communications":[174],"by":[175],"converging":[176],"within":[177],"100":[178],"episodes;":[179],"c)":[181],"average":[184],"throughput":[185],"performance":[186],"NOMA":[189],"better":[193],"conventional":[196],"orthogonal":[197],"system.":[201]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
