{"id":"https://openalex.org/W3161290381","doi":"https://doi.org/10.1109/wcnc49053.2021.9417275","title":"Deep Reinforcement Learning for RAN Optimization and Control","display_name":"Deep Reinforcement Learning for RAN Optimization and Control","publication_year":2021,"publication_date":"2021-03-29","ids":{"openalex":"https://openalex.org/W3161290381","doi":"https://doi.org/10.1109/wcnc49053.2021.9417275","mag":"3161290381"},"language":"en","primary_location":{"id":"doi:10.1109/wcnc49053.2021.9417275","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc49053.2021.9417275","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Wireless Communications and Networking Conference (WCNC)","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/A5100401995","display_name":"Yu Chen","orcid":"https://orcid.org/0000-0002-0260-6597"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yu Chen","raw_affiliation_strings":["Carnegie Mellon University, 5000 Forbes Ave. Pittsburgh, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, 5000 Forbes Ave. Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100332890","display_name":"Jie Chen","orcid":"https://orcid.org/0000-0002-1550-7328"},"institutions":[{"id":"https://openalex.org/I1283103587","display_name":"AT&T (United States)","ror":"https://ror.org/02bbd5539","country_code":"US","type":"company","lineage":["https://openalex.org/I1283103587"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jie Chen","raw_affiliation_strings":["AT&T Labs, 1 AT&T Way, Bedminster, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AT&T Labs, 1 AT&T Way, Bedminster, NJ, USA","institution_ids":["https://openalex.org/I1283103587"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043295445","display_name":"Ganesh Krishnamurthi","orcid":null},"institutions":[{"id":"https://openalex.org/I1283103587","display_name":"AT&T (United States)","ror":"https://ror.org/02bbd5539","country_code":"US","type":"company","lineage":["https://openalex.org/I1283103587"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ganesh Krishnamurthi","raw_affiliation_strings":["AT&T Labs, 1 AT&T Way, Bedminster, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AT&T Labs, 1 AT&T Way, Bedminster, NJ, USA","institution_ids":["https://openalex.org/I1283103587"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068365215","display_name":"Huijing Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I1283103587","display_name":"AT&T (United States)","ror":"https://ror.org/02bbd5539","country_code":"US","type":"company","lineage":["https://openalex.org/I1283103587"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Huijing Yang","raw_affiliation_strings":["AT&T Labs, 1 AT&T Way, Bedminster, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AT&T Labs, 1 AT&T Way, Bedminster, NJ, USA","institution_ids":["https://openalex.org/I1283103587"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040122379","display_name":"Huahui Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I1283103587","display_name":"AT&T (United States)","ror":"https://ror.org/02bbd5539","country_code":"US","type":"company","lineage":["https://openalex.org/I1283103587"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Huahui Wang","raw_affiliation_strings":["AT&T Labs, 1 AT&T Way, Bedminster, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AT&T Labs, 1 AT&T Way, Bedminster, NJ, USA","institution_ids":["https://openalex.org/I1283103587"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102281984","display_name":"Wenjie Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I1283103587","display_name":"AT&T (United States)","ror":"https://ror.org/02bbd5539","country_code":"US","type":"company","lineage":["https://openalex.org/I1283103587"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenjie Zhao","raw_affiliation_strings":["AT&T Labs, 1 AT&T Way, Bedminster, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AT&T Labs, 1 AT&T Way, Bedminster, NJ, USA","institution_ids":["https://openalex.org/I1283103587"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1017,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.41481844,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"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":0.9993000030517578,"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/T11158","display_name":"Wireless Networks and Protocols","score":0.9980999827384949,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/enodeb","display_name":"EnodeB","score":0.903951108455658},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8286566734313965},{"id":"https://openalex.org/keywords/testbed","display_name":"Testbed","score":0.7770984172821045},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7380065321922302},{"id":"https://openalex.org/keywords/radio-access-network","display_name":"Radio access network","score":0.6807109713554382},{"id":"https://openalex.org/keywords/ran","display_name":"Ran","score":0.5972594618797302},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5488555431365967},{"id":"https://openalex.org/keywords/network-performance","display_name":"Network performance","score":0.5485022664070129},{"id":"https://openalex.org/keywords/performance-indicator","display_name":"Performance indicator","score":0.5323368310928345},{"id":"https://openalex.org/keywords/controller","display_name":"Controller (irrigation)","score":0.4186423718929291},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.31013891100883484},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.21560272574424744},{"id":"https://openalex.org/keywords/user-equipment","display_name":"User equipment","score":0.15967217087745667},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.1255720555782318}],"concepts":[{"id":"https://openalex.org/C2776384992","wikidata":"https://www.wikidata.org/wiki/Q5379353","display_name":"EnodeB","level":4,"score":0.903951108455658},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8286566734313965},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.7770984172821045},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7380065321922302},{"id":"https://openalex.org/C106365562","wikidata":"https://www.wikidata.org/wiki/Q3078360","display_name":"Radio access network","level":4,"score":0.6807109713554382},{"id":"https://openalex.org/C160704184","wikidata":"https://www.wikidata.org/wiki/Q18031028","display_name":"Ran","level":2,"score":0.5972594618797302},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5488555431365967},{"id":"https://openalex.org/C203274722","wikidata":"https://www.wikidata.org/wiki/Q7001161","display_name":"Network performance","level":2,"score":0.5485022664070129},{"id":"https://openalex.org/C135510737","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance indicator","level":2,"score":0.5323368310928345},{"id":"https://openalex.org/C203479927","wikidata":"https://www.wikidata.org/wiki/Q5165939","display_name":"Controller (irrigation)","level":2,"score":0.4186423718929291},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.31013891100883484},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.21560272574424744},{"id":"https://openalex.org/C2781327853","wikidata":"https://www.wikidata.org/wiki/Q3552547","display_name":"User equipment","level":3,"score":0.15967217087745667},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.1255720555782318},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C207029474","wikidata":"https://www.wikidata.org/wiki/Q384018","display_name":"Mobile station","level":3,"score":0.0},{"id":"https://openalex.org/C68649174","wikidata":"https://www.wikidata.org/wiki/Q1379116","display_name":"Base station","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wcnc49053.2021.9417275","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc49053.2021.9417275","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Wireless Communications and Networking Conference (WCNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.49000000953674316,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1845972764","https://openalex.org/W2121863487","https://openalex.org/W2145339207","https://openalex.org/W2155968351","https://openalex.org/W2173248099","https://openalex.org/W2201581102","https://openalex.org/W2295318465","https://openalex.org/W2322852727","https://openalex.org/W2563965473","https://openalex.org/W2592120495","https://openalex.org/W2746553466","https://openalex.org/W2759880155","https://openalex.org/W2761873684","https://openalex.org/W2768597617","https://openalex.org/W2783729004","https://openalex.org/W2788005034","https://openalex.org/W2788862220","https://openalex.org/W2898035736","https://openalex.org/W2940469682","https://openalex.org/W2962846391","https://openalex.org/W2963079995","https://openalex.org/W2963376229","https://openalex.org/W2963477884","https://openalex.org/W2963864421","https://openalex.org/W2964043796","https://openalex.org/W2964291307","https://openalex.org/W2998266342","https://openalex.org/W3034687650","https://openalex.org/W3038498786","https://openalex.org/W6684921986","https://openalex.org/W6687681856","https://openalex.org/W6692846177","https://openalex.org/W6697299828","https://openalex.org/W6734554788"],"related_works":["https://openalex.org/W3018338683","https://openalex.org/W3037103914","https://openalex.org/W4315630224","https://openalex.org/W2902151128","https://openalex.org/W104163691","https://openalex.org/W2436923341","https://openalex.org/W4381187802","https://openalex.org/W4383173632","https://openalex.org/W4294306393","https://openalex.org/W3161290381"],"abstract_inverted_index":{"Due":[0],"to":[1,21,33,63,73,89,170],"the":[2,6,9,31,35,48,52,65,101,132,149,153,158,163],"high":[3],"variability":[4],"of":[5,30,51,131,152,165],"traffic":[7],"in":[8,121,174],"radio":[10],"access":[11,41],"network":[12,15,145,172,178],"(RAN),":[13],"fixed":[14],"configurations":[16,54],"are":[17,55],"not":[18,60,62],"flexible":[19],"enough":[20],"achieve":[22,110],"optimal":[23],"performance.":[24],"Our":[25,160],"vendors":[26,135],"provide":[27],"several":[28],"settings":[29],"eNodeB":[32,53,127],"optimize":[34],"RAN":[36,102,120,177],"performance,":[37],"such":[38],"as":[39],"media":[40],"control":[42,118],"scheduler,":[43],"loading":[44],"balance,":[45],"etc.":[46],"But":[47],"detailed":[49],"mechanisms":[50],"usually":[56],"very":[57],"complicated":[58],"and":[59,103,136],"disclosed,":[61],"mention":[64],"large":[66],"key":[67,154],"performance":[68,155,173],"indicators":[69,156],"(KPIs)":[70],"space":[71],"needed":[72],"be":[74],"considered.":[75],"These":[76],"make":[77],"constructing":[78],"a":[79,116,122,142,175],"simulator,":[80],"offline":[81],"tuning,":[82],"or":[83,97],"rule-based":[84],"solutions":[85],"difficult.":[86],"We":[87],"aim":[88],"build":[90,115,141],"an":[91],"intelligent":[92],"controller":[93],"without":[94,107],"strong":[95],"assumption":[96],"domain":[98],"knowledge":[99],"about":[100],"can":[104],"run":[105],"24/7":[106],"supervision.":[108],"To":[109],"this":[111],"goal,":[112],"we":[113,140],"first":[114],"closed-loop":[117],"testbed":[119],"lab":[123],"environment":[124],"with":[125,148],"one":[126,130],"provided":[128],"by":[129],"largest":[133],"wireless":[134],"four":[137],"smartphones.":[138],"Next,":[139],"double":[143],"Q":[144],"agent":[146],"trained":[147],"live":[150],"feedback":[151],"from":[157],"RAN.":[159],"work":[161],"proved":[162],"effectiveness":[164],"applying":[166],"deep":[167],"reinforcement":[168],"learning":[169],"improve":[171],"real":[176],"environment.":[179]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}