{"id":"https://openalex.org/W4393380340","doi":"https://doi.org/10.1109/ieeeconf59524.2023.10476972","title":"Open RAN LSTM Traffic Prediction and Slice Management Using Deep Reinforcement Learning","display_name":"Open RAN LSTM Traffic Prediction and Slice Management Using Deep Reinforcement Learning","publication_year":2023,"publication_date":"2023-10-29","ids":{"openalex":"https://openalex.org/W4393380340","doi":"https://doi.org/10.1109/ieeeconf59524.2023.10476972"},"language":"en","primary_location":{"id":"doi:10.1109/ieeeconf59524.2023.10476972","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf59524.2023.10476972","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 57th Asilomar Conference on Signals, Systems, and Computers","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/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":false,"raw_author_name":"Fatemeh Lotfi","raw_affiliation_strings":["Clemson University,Holcombe Department of Electrical and Computer Engineering,Clemson,SC,USA","Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Clemson University,Holcombe Department of Electrical and Computer Engineering,Clemson,SC,USA","institution_ids":["https://openalex.org/I8078737"]},{"raw_affiliation_string":"Holcombe Department of Electrical and Computer Engineering, Clemson University, 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","Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Clemson University,Holcombe Department of Electrical and Computer Engineering,Clemson,SC,USA","institution_ids":["https://openalex.org/I8078737"]},{"raw_affiliation_string":"Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA","institution_ids":["https://openalex.org/I8078737"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I8078737"],"apc_list":null,"apc_paid":null,"fwci":9.7719,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.98996715,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"646","last_page":"650"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.8730000257492065,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.8730000257492065,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T10320","display_name":"Neural Networks and Applications","score":0.7732999920845032,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8189573287963867},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7093291282653809},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5915898084640503},{"id":"https://openalex.org/keywords/ran","display_name":"Ran","score":0.5831066966056824},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41577285528182983},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35341209173202515},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.1091565191745758}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8189573287963867},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7093291282653809},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5915898084640503},{"id":"https://openalex.org/C160704184","wikidata":"https://www.wikidata.org/wiki/Q18031028","display_name":"Ran","level":2,"score":0.5831066966056824},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41577285528182983},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35341209173202515},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.1091565191745758}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ieeeconf59524.2023.10476972","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf59524.2023.10476972","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 57th Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5004918769","display_name":null,"funder_award_id":"CNS-2202972,CNS-2318726,CNS-2232048","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W3216563077","https://openalex.org/W4283808627","https://openalex.org/W4290997040","https://openalex.org/W4313067193","https://openalex.org/W4315629987","https://openalex.org/W4315777437","https://openalex.org/W4327522621","https://openalex.org/W4327767831","https://openalex.org/W4363648989","https://openalex.org/W4367016879","https://openalex.org/W4368755382","https://openalex.org/W4376480872","https://openalex.org/W4386474131","https://openalex.org/W4388675112","https://openalex.org/W4390792742","https://openalex.org/W4392152323","https://openalex.org/W6747473740","https://openalex.org/W6852410624"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3046775127","https://openalex.org/W3082895349"],"abstract_inverted_index":{"With":[0],"emerging":[1],"applications":[2],"such":[3],"as":[4,24,142],"autonomous":[5],"driving,":[6],"smart":[7,10],"cities,":[8],"and":[9,21,68,137],"factories,":[11],"network":[12,36,121],"slicing":[13,75],"has":[14],"become":[15],"an":[16],"essential":[17],"component":[18],"of":[19,27,41,61,89,132,144],"5G":[20],"beyond":[22],"networks":[23],"a":[25,30,45,48,70,93,145],"means":[26],"catering":[28],"to":[29,73,84,104,112],"service-aware":[31],"network.":[32],"However,":[33],"managing":[34],"different":[35],"slices":[37],"while":[38],"maintaining":[39],"quality":[40],"services":[42],"(QoS)":[43],"is":[44,102],"challenge":[46],"in":[47,65,120,124],"dynamic":[49,110,146],"environment.":[50],"To":[51],"address":[52],"this":[53,55],"issue,":[54],"paper":[56],"leverages":[57],"the":[58,86,90,109,113,130,134],"heterogeneous":[59],"experiences":[60],"distributed":[62,78,138],"units":[63],"(DUs)":[64],"ORAN":[66,74],"systems":[67],"introduces":[69],"novel":[71],"approach":[72],"xApp":[76],"using":[77,133],"deep":[79],"reinforcement":[80],"learning":[81],"(DDRL).":[82],"Additionally,":[83],"enhance":[85],"decision-making":[87],"performance":[88],"RL":[91],"agent,":[92],"prediction":[94,135],"rApp":[95,136],"based":[96],"on":[97],"long":[98],"short-term":[99],"memory":[100],"(LSTM)":[101],"incorporated":[103],"provide":[105],"additional":[106],"information":[107,140],"from":[108],"environment":[111],"xApp.":[114,147],"Simulation":[115],"results":[116],"demonstrate":[117],"significant":[118],"improvements":[119],"performance,":[122],"particularly":[123],"reducing":[125],"QoS":[126],"violations.":[127],"This":[128],"emphasizes":[129],"importance":[131],"actors'":[139],"jointly":[141],"part":[143]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}