{"id":"https://openalex.org/W4280559110","doi":"https://doi.org/10.1109/wcnc51071.2022.9771643","title":"5G Network Slice Admission Control Using Optimization and Reinforcement Learning","display_name":"5G Network Slice Admission Control Using Optimization and Reinforcement Learning","publication_year":2022,"publication_date":"2022-04-10","ids":{"openalex":"https://openalex.org/W4280559110","doi":"https://doi.org/10.1109/wcnc51071.2022.9771643"},"language":"en","primary_location":{"id":"doi:10.1109/wcnc51071.2022.9771643","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc51071.2022.9771643","pdf_url":null,"source":{"id":"https://openalex.org/S4363607776","display_name":"2022 IEEE Wireless Communications and Networking Conference (WCNC)","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":"2022 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/A5032719273","display_name":"Md Ariful Haque","orcid":"https://orcid.org/0000-0002-7224-4237"},"institutions":[{"id":"https://openalex.org/I81365321","display_name":"Old Dominion University","ror":"https://ror.org/04zjtrb98","country_code":"US","type":"education","lineage":["https://openalex.org/I81365321"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Md Ariful Haque","raw_affiliation_strings":["Old Dominion University,Computational Modeling and Simulation Engineering,Norfolk,VA,USA","Computational Modeling and Simulation Engineering, Old Dominion University, Norfolk, VA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Old Dominion University,Computational Modeling and Simulation Engineering,Norfolk,VA,USA","institution_ids":["https://openalex.org/I81365321"]},{"raw_affiliation_string":"Computational Modeling and Simulation Engineering, Old Dominion University, Norfolk, VA, USA","institution_ids":["https://openalex.org/I81365321"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034454912","display_name":"Vassilka Kirova","orcid":"https://orcid.org/0009-0006-2252-7956"},"institutions":[{"id":"https://openalex.org/I2738502077","display_name":"Nokia (Finland)","ror":"https://ror.org/04pkc8m17","country_code":"FI","type":"company","lineage":["https://openalex.org/I2738502077"]},{"id":"https://openalex.org/I72090969","display_name":"Nokia (United States)","ror":"https://ror.org/038km2573","country_code":"US","type":"company","lineage":["https://openalex.org/I2738502077","https://openalex.org/I72090969"]}],"countries":["FI","US"],"is_corresponding":false,"raw_author_name":"Vassilka Kirova","raw_affiliation_strings":["Nokia Bell Labs Consulting,Murray Hill,NJ,USA","Nokia Bell Labs Consulting, Murray Hill, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nokia Bell Labs Consulting,Murray Hill,NJ,USA","institution_ids":["https://openalex.org/I2738502077"]},{"raw_affiliation_string":"Nokia Bell Labs Consulting, Murray Hill, NJ, USA","institution_ids":["https://openalex.org/I72090969"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.0439,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.95267721,"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":"854","last_page":"859"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10796","display_name":"Cooperative Communication and Network Coding","score":0.9950000047683716,"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"}},"topics":[{"id":"https://openalex.org/T10796","display_name":"Cooperative Communication and Network Coding","score":0.9950000047683716,"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"}},{"id":"https://openalex.org/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.9937999844551086,"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/T10714","display_name":"Software-Defined Networks and 5G","score":0.9922000169754028,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.8734012842178345},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7374922633171082},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.5336006283760071},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48552942276000977},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.4383031129837036},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10792192816734314}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8734012842178345},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7374922633171082},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.5336006283760071},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48552942276000977},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.4383031129837036},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10792192816734314},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wcnc51071.2022.9771643","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc51071.2022.9771643","pdf_url":null,"source":{"id":"https://openalex.org/S4363607776","display_name":"2022 IEEE Wireless Communications and Networking Conference (WCNC)","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":"2022 IEEE Wireless Communications and Networking Conference (WCNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1969363607","https://openalex.org/W2124628131","https://openalex.org/W2605961225","https://openalex.org/W2751757814","https://openalex.org/W2963232083","https://openalex.org/W3098945132","https://openalex.org/W3119836567","https://openalex.org/W3130426370"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4310083477","https://openalex.org/W2328553770","https://openalex.org/W2920061524","https://openalex.org/W1977959518","https://openalex.org/W2038908348","https://openalex.org/W2107890255","https://openalex.org/W2106552856"],"abstract_inverted_index":{"Network":[0,131],"slicing":[1],"can":[2,157],"enable":[3],"operators":[4],"to":[5,111,116,164],"maximize":[6,165],"revenue":[7,60,92],"by":[8],"optimizing":[9],"the":[10,42,69,80,85,95,102,122,128,142],"utilization":[11],"of":[12,44,55],"network":[13,30,166],"resources.":[14],"As":[15],"a":[16,53,62],"result,":[17],"slice":[18,45],"admission":[19,46,70],"control":[20],"and":[21,51,66,149,156],"resource":[22],"allocation":[23],"decisions":[24,119],"become":[25],"critical":[26],"tasks":[27],"for":[28],"5G":[29],"orchestration.":[31],"In":[32,101],"this":[33],"work,":[34],"we":[35,105],"examine":[36],"two":[37,152],"complimentary":[38],"approaches":[39,153],"that":[40,57,84,127],"address":[41],"challenge":[43],"control:":[47],"(1)":[48],"dynamically":[49,117],"assessing":[50],"accepting":[52,94],"set":[54],"slices":[56,96],"generate":[58],"greater":[59],"using":[61,72],"queue-based":[63],"optimization":[64,87],"algorithm,":[65],"(2)":[67],"automating":[68],"decision":[71],"reinforcement":[73,108],"learning":[74,109],"(RL).":[75],"The":[76,151],"simulation":[77],"results":[78,125],"from":[79],"first":[81],"approach":[82],"show":[83,126],"proposed":[86],"algorithm":[88,133],"always":[89],"delivers":[90],"higher":[91,135],"than":[93,141],"in":[97],"any":[98],"other":[99,143],"order.":[100],"second":[103],"approach,":[104],"utilize":[106],"several":[107],"algorithms":[110,145],"train":[112],"an":[113],"agent":[114],"(orchestrator)":[115],"make":[118],"based":[120],"on":[121],"environment.":[123],"Our":[124],"Deep":[129],"Q":[130],"(DQN)":[132],"provides":[134],"cumulative":[136,139],"rewards":[137],"(i.e.,":[138],"revenue)":[140],"tested":[144],"(SARSA,":[146],"Expected":[147],"SARSA,":[148],"Q-Learning).":[150],"are":[154],"complementary,":[155],"be":[158],"deployed":[159],"either":[160],"separately":[161],"or":[162],"simultaneously":[163],"operator’s":[167],"revenue.":[168]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}