{"id":"https://openalex.org/W4400276418","doi":"https://doi.org/10.1109/wcnc57260.2024.10571107","title":"Unveiling the Effects of Experience Replay on Deep Reinforcement Learning-based Power Allocation in Wireless Networks","display_name":"Unveiling the Effects of Experience Replay on Deep Reinforcement Learning-based Power Allocation in Wireless Networks","publication_year":2024,"publication_date":"2024-04-21","ids":{"openalex":"https://openalex.org/W4400276418","doi":"https://doi.org/10.1109/wcnc57260.2024.10571107"},"language":"en","primary_location":{"id":"doi:10.1109/wcnc57260.2024.10571107","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/wcnc57260.2024.10571107","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE Wireless Communications and Networking Conference (WCNC)","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/A5093111426","display_name":"Amna Kopic","orcid":"https://orcid.org/0009-0008-9151-2875"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Amna Kopic","raw_affiliation_strings":["Chair for Distributed Signal Processing RWTH Aachen University,Aachen,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chair for Distributed Signal Processing RWTH Aachen University,Aachen,Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075029993","display_name":"Erma Perenda","orcid":"https://orcid.org/0000-0003-4282-2507"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Erma Perenda","raw_affiliation_strings":["Chair for Distributed Signal Processing RWTH Aachen University,Aachen,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chair for Distributed Signal Processing RWTH Aachen University,Aachen,Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026448728","display_name":"Haris Gacanin","orcid":"https://orcid.org/0000-0003-3168-8883"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Haris Gacanin","raw_affiliation_strings":["Chair for Distributed Signal Processing RWTH Aachen University,Aachen,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chair for Distributed Signal Processing RWTH Aachen University,Aachen,Germany","institution_ids":["https://openalex.org/I887968799"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I887968799"],"apc_list":null,"apc_paid":null,"fwci":0.9278,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.6428609,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.9812999963760376,"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"}},"topics":[{"id":"https://openalex.org/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.9812999963760376,"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/T11392","display_name":"Energy Harvesting in Wireless Networks","score":0.9778000116348267,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.849184513092041},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7052867412567139},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.5954185724258423},{"id":"https://openalex.org/keywords/wireless-network","display_name":"Wireless network","score":0.5307279229164124},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.5012662410736084},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.35030633211135864},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3254830837249756},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.2889365553855896}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.849184513092041},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7052867412567139},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.5954185724258423},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.5307279229164124},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.5012662410736084},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.35030633211135864},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3254830837249756},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.2889365553855896},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wcnc57260.2024.10571107","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/wcnc57260.2024.10571107","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE Wireless Communications and Networking Conference (WCNC)","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":19,"referenced_works":["https://openalex.org/W1970830346","https://openalex.org/W2497016966","https://openalex.org/W2768629321","https://openalex.org/W2772721022","https://openalex.org/W2784277076","https://openalex.org/W2788350651","https://openalex.org/W2937571395","https://openalex.org/W2963186244","https://openalex.org/W2968018638","https://openalex.org/W2969195240","https://openalex.org/W3036160090","https://openalex.org/W4213007911","https://openalex.org/W4250897953","https://openalex.org/W4284962466","https://openalex.org/W4306786778","https://openalex.org/W4387869974","https://openalex.org/W4387872840","https://openalex.org/W6734678876","https://openalex.org/W6746809867"],"related_works":["https://openalex.org/W4306904969","https://openalex.org/W2138720691","https://openalex.org/W4362501864","https://openalex.org/W4380318855","https://openalex.org/W2031695474","https://openalex.org/W2024136090","https://openalex.org/W2586732548","https://openalex.org/W3049728571","https://openalex.org/W2964765435","https://openalex.org/W2908230797"],"abstract_inverted_index":{"Deep":[0],"reinforcement":[1,105],"learning":[2,17,106,155],"has":[3,63],"emerged":[4],"as":[5,13],"a":[6,101,112,136],"pow-erful":[7],"tool":[8],"for":[9,123],"dynamic":[10,86],"power":[11,87,96],"allocation,":[12],"it":[14,164],"allows":[15],"continuous":[16],"and":[18,39,51],"realtime":[19],"responsiveness":[20],"to":[21,70,127,160],"environmental":[22],"changes":[23],"through":[24],"its":[25],"core":[26],"element,":[27],"experience":[28,57,79],"replay.":[29],"Experience":[30],"replay":[31,37,58,80,119,138],"involves":[32],"two":[33],"critical":[34],"hyperparameters:":[35],"the":[36,54,76,83,95,118,124,145,154],"buffer":[38,120,139],"mini-batch":[40],"sizes.":[41],"While":[42],"state-of-the-art":[43],"solutions":[44],"primarily":[45],"concentrate":[46],"on":[47,60],"designing":[48],"input":[49],"features":[50],"reward-shaping":[52],"methods,":[53],"impact":[55],"of":[56,78,85,114],"parameters":[59,81],"system":[61,142],"performance":[62],"often":[64],"been":[65],"overlooked.":[66],"This":[67],"paper":[68],"aims":[69],"address":[71,94],"this":[72],"gap":[73],"by":[74],"exploring":[75],"effects":[77],"in":[82,89,117,147],"context":[84],"allocation":[88,97],"multi-carrier":[90],"wireless":[91],"systems.":[92],"To":[93],"problem,":[98],"we":[99],"propose":[100],"multi-agent":[102],"cooperative":[103],"deep":[104],"framework.":[107],"The":[108],"results":[109],"show":[110],"that":[111],"minimum":[113],"2000":[115],"experiences":[116,134],"is":[121],"necessary":[122],"proposed":[125],"solution":[126],"outperform":[128],"conventional":[129],"approaches.":[130],"Moreover,":[131],"many":[132],"obsolete":[133],"within":[135],"larger":[137],"slightly":[140],"decrease":[141],"performance.":[143,166],"Interestingly,":[144],"increase":[146],"batch":[148],"size":[149],"does":[150],"not":[151],"significantly":[152],"affect":[153],"models'":[156],"training":[157],"time":[158],"due":[159],"parallel":[161],"execution,":[162],"yet,":[163],"improves":[165]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}