{"id":"https://openalex.org/W4206986183","doi":"https://doi.org/10.1109/gcwkshps52748.2021.9681936","title":"O-RAN AI/ML Workflow Implementation of Personalized Network Optimization via Reinforcement Learning","display_name":"O-RAN AI/ML Workflow Implementation of Personalized Network Optimization via Reinforcement Learning","publication_year":2021,"publication_date":"2021-12-01","ids":{"openalex":"https://openalex.org/W4206986183","doi":"https://doi.org/10.1109/gcwkshps52748.2021.9681936"},"language":"en","primary_location":{"id":"doi:10.1109/gcwkshps52748.2021.9681936","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcwkshps52748.2021.9681936","pdf_url":null,"source":{"id":"https://openalex.org/S4363605397","display_name":"2021 IEEE Globecom Workshops (GC Wkshps)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Globecom Workshops (GC Wkshps)","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/A5014143566","display_name":"Hoejoo Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Hoejoo Lee","raw_affiliation_strings":["Samsung Research, Samsung Electronics, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Samsung Research, Samsung Electronics, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068607217","display_name":"Youngcheol Jang","orcid":null},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Youngcheol Jang","raw_affiliation_strings":["Samsung Research, Samsung Electronics, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Samsung Research, Samsung Electronics, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045698619","display_name":"Juhwan Song","orcid":null},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Juhwan Song","raw_affiliation_strings":["Samsung Research, Samsung Electronics, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Samsung Research, Samsung Electronics, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015713068","display_name":"Hun-je Yeon","orcid":null},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hunje Yeon","raw_affiliation_strings":["Samsung Research, Samsung Electronics, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Samsung Research, Samsung Electronics, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I2250650973"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5014143566"],"corresponding_institution_ids":["https://openalex.org/I2250650973"],"apc_list":null,"apc_paid":null,"fwci":8.2369,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.99121744,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"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":0.9994999766349792,"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":0.9994999766349792,"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":0.9994000196456909,"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/T13052","display_name":"Molecular Communication and Nanonetworks","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/workflow","display_name":"Workflow","score":0.8037626147270203},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7951861619949341},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7846928834915161},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.7282112836837769},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.6133412718772888},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.5377739667892456},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44018489122390747},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4145796298980713},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.1902393102645874},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.1755792200565338},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.12180545926094055}],"concepts":[{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.8037626147270203},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7951861619949341},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7846928834915161},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7282112836837769},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.6133412718772888},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.5377739667892456},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44018489122390747},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4145796298980713},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.1902393102645874},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.1755792200565338},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.12180545926094055},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C155202549","wikidata":"https://www.wikidata.org/wiki/Q178803","display_name":"International trade","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/gcwkshps52748.2021.9681936","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcwkshps52748.2021.9681936","pdf_url":null,"source":{"id":"https://openalex.org/S4363605397","display_name":"2021 IEEE Globecom Workshops (GC Wkshps)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Globecom Workshops (GC Wkshps)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W41554520","https://openalex.org/W1902748306","https://openalex.org/W2062403143","https://openalex.org/W2117638787","https://openalex.org/W2896431659","https://openalex.org/W3103243862","https://openalex.org/W3134779518","https://openalex.org/W4288113443","https://openalex.org/W4297736277","https://openalex.org/W4299518610"],"related_works":["https://openalex.org/W2006651773","https://openalex.org/W2027050655","https://openalex.org/W3028244590","https://openalex.org/W2014369232","https://openalex.org/W2050078012","https://openalex.org/W3122042562","https://openalex.org/W4254349500","https://openalex.org/W2060761133","https://openalex.org/W2375218795","https://openalex.org/W2393010557"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"study":[4],"AI-based":[5],"RAN":[6,37],"technology":[7],"for":[8,88,146],"5G":[9],"and":[10,17,79,141],"6G":[11],"networks":[12],"that":[13],"are":[14],"more":[15,28],"complex":[16],"difficult":[18],"to":[19,24,43,70,80,107,155],"analyze":[20],"than":[21],"previous":[22],"generations":[23],"make":[25],"the":[26,44,53,62,72,114,131,138,143,148,156,160,171],"network":[27],"intelligent.":[29],"We":[30,50,65,91,129],"implement":[31],"a":[32,82,119,126],"reference":[33],"AI/ML":[34,45],"workflow":[35,46],"using":[36,150,170],"Intelligent":[38],"Controller":[39],"(RIC)":[40],"by":[41,136,166],"referring":[42],"architecture":[47],"of":[48,55,133],"O-RAN.":[49],"focus":[51],"on":[52,61],"establishment":[54],"an":[56,76],"online":[57,89,151],"training":[58,86,113,144],"environment":[59],"based":[60],"RIC":[63],"platform.":[64],"use":[66],"various":[67],"open-source":[68],"platforms":[69],"serve":[71],"ML":[73],"model":[74,98,115,139,149,157],"as":[75],"inference":[77],"service":[78],"build":[81],"Machine":[83],"Learning":[84,96],"(ML)":[85],"pipeline":[87,145],"training.":[90],"train":[92],"our":[93,134],"own":[94],"Reinforcement":[95],"(RL)":[97],"which":[99],"controls":[100],"function":[101],"parameters":[102],"in":[103,125],"Distributed":[104],"Unit":[105],"(DU)":[106],"maximize":[108],"total":[109,161],"cell":[110,162],"throughput.":[111],"After":[112],"with":[116],"data":[117],"from":[118],"specific":[120],"cell,":[121],"it":[122],"is":[123],"deployed":[124],"different":[127],"environment.":[128],"demonstrate":[130],"effectiveness":[132],"proposal":[135],"optimizing":[137],"performance":[140],"executing":[142],"retraining":[147],"workflow.":[152],"As":[153],"compared":[154],"before":[158],"retraining,":[159],"throughput":[163],"has":[164],"increased":[165],"19.4%":[167],"when":[168],"controlled":[169],"retrained":[172],"model.":[173]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":3}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-10-10T00:00:00"}
