{"id":"https://openalex.org/W4414538751","doi":"https://doi.org/10.1109/icc52391.2025.11161572","title":"LLM-Augmented Deep Reinforcement Learning for Dynamic O-RAN Network Slicing","display_name":"LLM-Augmented Deep Reinforcement Learning for Dynamic O-RAN Network Slicing","publication_year":2025,"publication_date":"2025-06-08","ids":{"openalex":"https://openalex.org/W4414538751","doi":"https://doi.org/10.1109/icc52391.2025.11161572"},"language":"en","primary_location":{"id":"doi:10.1109/icc52391.2025.11161572","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc52391.2025.11161572","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2025 - IEEE International Conference on Communications","raw_type":"proceedings-article"},"type":"article","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/A5065027814","display_name":"Fatemeh Lotfi","orcid":"https://orcid.org/0009-0009-1691-0029"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Fatemeh Lotfi","raw_affiliation_strings":["Clemson University,Holcombe Department of Electrical and Computer Engineering,Clemson,SC,USA"],"affiliations":[{"raw_affiliation_string":"Clemson University,Holcombe Department of Electrical and Computer Engineering,Clemson,SC,USA","institution_ids":["https://openalex.org/I8078737"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025017577","display_name":"Hossein Rajoli","orcid":null},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hossein Rajoli","raw_affiliation_strings":["Clemson University,Holcombe Department of Electrical and Computer Engineering,Clemson,SC,USA"],"affiliations":[{"raw_affiliation_string":"Clemson University,Holcombe Department of Electrical and Computer Engineering,Clemson,SC,USA","institution_ids":["https://openalex.org/I8078737"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035395012","display_name":"Fatemeh Afghah","orcid":"https://orcid.org/0000-0002-2315-1173"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fatemeh Afghah","raw_affiliation_strings":["Clemson University,Holcombe Department of Electrical and Computer Engineering,Clemson,SC,USA"],"affiliations":[{"raw_affiliation_string":"Clemson University,Holcombe Department of Electrical and Computer Engineering,Clemson,SC,USA","institution_ids":["https://openalex.org/I8078737"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5065027814"],"corresponding_institution_ids":["https://openalex.org/I8078737"],"apc_list":null,"apc_paid":null,"fwci":16.5509,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.98770111,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3827","last_page":"3832"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13731","display_name":"Advanced Computing and Algorithms","score":0.9254000186920166,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13731","display_name":"Advanced Computing and Algorithms","score":0.9254000186920166,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8059999942779541},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5590999722480774},{"id":"https://openalex.org/keywords/grasp","display_name":"GRASP","score":0.5467000007629395},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4431999921798706},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4424999952316284},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.42649999260902405},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3788999915122986},{"id":"https://openalex.org/keywords/quality-of-service","display_name":"Quality of service","score":0.37700000405311584},{"id":"https://openalex.org/keywords/context-awareness","display_name":"Context awareness","score":0.35100001096725464}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8059999942779541},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.746399998664856},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5590999722480774},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.5467000007629395},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5462999939918518},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4431999921798706},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4424999952316284},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.42649999260902405},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3788999915122986},{"id":"https://openalex.org/C5119721","wikidata":"https://www.wikidata.org/wiki/Q220501","display_name":"Quality of service","level":2,"score":0.37700000405311584},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3644999861717224},{"id":"https://openalex.org/C2781368080","wikidata":"https://www.wikidata.org/wiki/Q501688","display_name":"Context awareness","level":3,"score":0.35100001096725464},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.3416000008583069},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C13540734","wikidata":"https://www.wikidata.org/wiki/Q5318996","display_name":"Dynamic network analysis","level":2,"score":0.32749998569488525},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3156000077724457},{"id":"https://openalex.org/C203479927","wikidata":"https://www.wikidata.org/wiki/Q5165939","display_name":"Controller (irrigation)","level":2,"score":0.3091000020503998},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.3037000000476837},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30079999566078186},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C74072328","wikidata":"https://www.wikidata.org/wiki/Q1142726","display_name":"Intelligent agent","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C106365562","wikidata":"https://www.wikidata.org/wiki/Q3078360","display_name":"Radio access network","level":4,"score":0.2824000120162964},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C91071405","wikidata":"https://www.wikidata.org/wiki/Q1413145","display_name":"Program slicing","level":3,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icc52391.2025.11161572","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc52391.2025.11161572","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2025 - IEEE International Conference on Communications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8577712339","display_name":null,"funder_award_id":"CNS-2202972,CNS- 2318726,CNS2232048","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W3005761304","https://openalex.org/W4315629987","https://openalex.org/W4317796310","https://openalex.org/W4390660070","https://openalex.org/W4392152323","https://openalex.org/W4392152396","https://openalex.org/W4393380340","https://openalex.org/W4402262734","https://openalex.org/W4410227715","https://openalex.org/W4410227886"],"related_works":[],"abstract_inverted_index":{"Advanced":[0],"wireless":[1],"networks":[2,19,66],"must":[3],"be":[4],"highly":[5],"dynamic":[6,65],"and":[7,44,81,130,154],"capable":[8],"of":[9,17,86],"managing":[10,64],"heterogeneous":[11],"service":[12],"demands.":[13],"A":[14],"key":[15],"feature":[16],"these":[18],"is":[20,24],"network":[21,31],"slicing,":[22],"which":[23],"supported":[25],"in":[26],"the":[27,50,95,125,135,140,149,178],"next-generation":[28],"radio":[29],"access":[30],"(RAN)":[32],"architecture,":[33],"such":[34],"as":[35,148],"open":[36],"RAN":[37],"(O-RAN),":[38],"by":[39],"leveraging":[40],"artificial":[41],"intelligence":[42],"(AI)":[43],"machine":[45],"learning":[46,58],"(ML)":[47],"approaches":[48],"within":[49],"ran":[51],"intelligent":[52],"controller":[53],"(RIC)":[54],"modules.":[55],"Deep":[56],"reinforcement":[57],"(DRL)":[59],"has":[60],"much":[61],"potential":[62],"for":[63,91],"but":[67],"often":[68],"needs":[69],"to":[70,93,108,121,168],"improve":[71],"when":[72],"dealing":[73],"with":[74],"unstructured,":[75],"multi-modal":[76],"data":[77,87],"like":[78],"RF":[79],"signals":[80],"QoS":[82],"metrics.":[83],"This":[84,138,158],"kind":[85],"makes":[88],"it":[89],"challenging":[90],"DRL":[92,110,141,171],"grasp":[94,127],"high-level":[96],"context":[97,129],"fully.":[98],"To":[99],"tackle":[100],"this,":[101],"we're":[102],"using":[103],"large":[104],"language":[105],"models":[106],"(LLMs)":[107],"give":[109],"a":[111,161],"richer,":[112],"more":[113,132,152],"meaningful":[114],"state":[115,163],"representation.":[116],"LLMs":[117],"add":[118],"semantic":[119],"layers":[120],"raw":[122],"data,":[123],"helping":[124],"agent":[126,142],"deeper":[128],"think":[131],"strategically":[133],"over":[134,156],"long":[136],"term.":[137],"way,":[139],"can":[143],"make":[144],"smarter":[145],"decisions":[146],"even":[147],"environment":[150],"becomes":[151],"complex":[153],"changes":[155],"time.":[157],"paper":[159],"proposes":[160],"task-related":[162],"representation":[164],"that":[165,177],"employs":[166],"LLM":[167],"augment":[169],"multi-agent":[170],"(MARL)":[172],"approaches.":[173],"Simulation":[174],"results":[175],"demonstrate":[176],"proposed":[179],"approach":[180],"significantly":[181],"outperforms":[182],"related":[183],"baselines.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-04-23T09:07:50.710637","created_date":"2025-10-10T00:00:00"}