{"id":"https://openalex.org/W4226321819","doi":"https://doi.org/10.1109/gcwkshps52748.2021.9682032","title":"Reinforcement Learning Random Access for Delay-Constrained Heterogeneous Wireless Networks: A Two-User Case","display_name":"Reinforcement Learning Random Access for Delay-Constrained Heterogeneous Wireless Networks: A Two-User Case","publication_year":2021,"publication_date":"2021-12-01","ids":{"openalex":"https://openalex.org/W4226321819","doi":"https://doi.org/10.1109/gcwkshps52748.2021.9682032"},"language":"en","primary_location":{"id":"doi:10.1109/gcwkshps52748.2021.9682032","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcwkshps52748.2021.9682032","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/A5005325602","display_name":"Danzhou Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Danzhou Wu","raw_affiliation_strings":["Shenzhen University"],"affiliations":[{"raw_affiliation_string":"Shenzhen University","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072868669","display_name":"Lei Deng","orcid":"https://orcid.org/0000-0002-0584-1146"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Deng","raw_affiliation_strings":["Shenzhen University"],"affiliations":[{"raw_affiliation_string":"Shenzhen University","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100629531","display_name":"Zilong Liu","orcid":"https://orcid.org/0000-0002-5851-4261"},"institutions":[{"id":"https://openalex.org/I110002522","display_name":"University of Essex","ror":"https://ror.org/02nkf1q06","country_code":"GB","type":"education","lineage":["https://openalex.org/I110002522"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Zilong Liu","raw_affiliation_strings":["University of Essex"],"affiliations":[{"raw_affiliation_string":"University of Essex","institution_ids":["https://openalex.org/I110002522"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101907018","display_name":"Yijin Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yijin Zhang","raw_affiliation_strings":["Nanjing University of Science and Technology"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023807587","display_name":"Yunghsiang S. Han","orcid":"https://orcid.org/0000-0002-3592-1681"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunghsiang S. Han","raw_affiliation_strings":["University of Electronic Science and Technology of China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5005325602"],"corresponding_institution_ids":["https://openalex.org/I180726961"],"apc_list":null,"apc_paid":null,"fwci":1.5466,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.82846535,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"100","issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11158","display_name":"Wireless Networks and Protocols","score":0.9997000098228455,"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/T11158","display_name":"Wireless Networks and Protocols","score":0.9997000098228455,"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/T11932","display_name":"Wireless Body Area Networks","score":0.9997000098228455,"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"}},{"id":"https://openalex.org/T11392","display_name":"Energy Harvesting in Wireless Networks","score":0.9991000294685364,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8381685614585876},{"id":"https://openalex.org/keywords/random-access","display_name":"Random access","score":0.6839410662651062},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6831860542297363},{"id":"https://openalex.org/keywords/aloha","display_name":"Aloha","score":0.6821030378341675},{"id":"https://openalex.org/keywords/wireless-network","display_name":"Wireless network","score":0.6404303312301636},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.6381088495254517},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.6027941107749939},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.54892498254776},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.4943854808807373},{"id":"https://openalex.org/keywords/heterogeneous-network","display_name":"Heterogeneous network","score":0.49298903346061707},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.44230714440345764},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.4266689419746399},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.4253641963005066},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4241792559623718},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.41782742738723755},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.13657733798027039},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.10582989454269409}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8381685614585876},{"id":"https://openalex.org/C101722063","wikidata":"https://www.wikidata.org/wiki/Q218825","display_name":"Random access","level":2,"score":0.6839410662651062},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6831860542297363},{"id":"https://openalex.org/C2776398200","wikidata":"https://www.wikidata.org/wiki/Q508880","display_name":"Aloha","level":4,"score":0.6821030378341675},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.6404303312301636},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.6381088495254517},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.6027941107749939},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.54892498254776},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.4943854808807373},{"id":"https://openalex.org/C158207573","wikidata":"https://www.wikidata.org/wiki/Q5747224","display_name":"Heterogeneous network","level":4,"score":0.49298903346061707},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.44230714440345764},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.4266689419746399},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.4253641963005066},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4241792559623718},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.41782742738723755},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.13657733798027039},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.10582989454269409},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/gcwkshps52748.2021.9682032","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcwkshps52748.2021.9682032","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":[{"id":"https://metadata.un.org/sdg/16","score":0.5,"display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G1769386045","display_name":null,"funder_award_id":"62071236","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6797587438","display_name":null,"funder_award_id":"61671007","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8692688642","display_name":null,"funder_award_id":"61902256","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G953098173","display_name":null,"funder_award_id":"30920021127","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320316083","display_name":"Tencent","ror":"https://ror.org/00hhjss72"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320325571","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W41554520","https://openalex.org/W1549353711","https://openalex.org/W2079705392","https://openalex.org/W2101242010","https://openalex.org/W2144256528","https://openalex.org/W2158914121","https://openalex.org/W2334782222","https://openalex.org/W2759063821","https://openalex.org/W2784711283","https://openalex.org/W2897113583","https://openalex.org/W2898035736","https://openalex.org/W2922273628","https://openalex.org/W2938939211","https://openalex.org/W2947945231","https://openalex.org/W3040508955","https://openalex.org/W3107130550","https://openalex.org/W3109528179","https://openalex.org/W3115241214","https://openalex.org/W4226321819","https://openalex.org/W6675043515","https://openalex.org/W6761234090","https://openalex.org/W6786492694"],"related_works":["https://openalex.org/W2740103453","https://openalex.org/W2767687907","https://openalex.org/W4302323979","https://openalex.org/W4234150174","https://openalex.org/W2543616084","https://openalex.org/W2952266750","https://openalex.org/W2123879133","https://openalex.org/W2044408233","https://openalex.org/W2392999663","https://openalex.org/W2560075810"],"abstract_inverted_index":{"In":[0],"this":[1,21,77],"paper,":[2],"we":[3,24,57],"investigate":[4],"the":[5,61,66,86,95,114,142,181],"random":[6,62,160],"access":[7,37,63,161],"problem":[8,78],"for":[9,191],"a":[10,16,26,41,100,126,134],"delay-constrained":[11,33],"heterogeneous":[12,193],"wireless":[13,45,194],"network.":[14,195],"As":[15],"first":[17,124],"attempt":[18],"to":[19,35,59,93,110,132,140,155,170],"study":[20],"new":[22],"problem,":[23],"consider":[25],"network":[27],"with":[28],"two":[29],"users":[30],"who":[31],"deliver":[32],"traffic":[34],"an":[36,157],"point":[38],"(AP)":[39],"via":[40],"common":[42],"unreliable":[43],"collision":[44],"channel.":[46],"By":[47],"assuming":[48],"that":[49,80,172],"one":[50],"user":[51,53,68,70,81,89],"(called":[52,69],"1)":[54],"adopts":[55],"ALOHA,":[56],"aim":[58],"optimize":[60],"scheme":[64,190],"of":[65,76,88,102,145],"other":[67],"2).":[71],"The":[72],"most":[73],"intriguing":[74],"part":[75],"is":[79,106,187],"2":[82],"does":[83],"not":[84],"know":[85],"information":[87],"1":[90],"but":[91],"needs":[92],"maximize":[94],"system":[96],"timely":[97],"throughput.":[98],"Such":[99],"paradigm":[101],"collaboratively":[103],"sharing":[104],"spectrum":[105],"envisioned":[107],"by":[108],"DARPA":[109],"better":[111,178],"dynamically":[112],"match":[113],"supply":[115],"and":[116,177],"demand":[117],"in":[118],"future":[119],"networks":[120],"[1],":[121],"[2].":[122],"We":[123,149,165],"propose":[125],"Markov":[127],"Decision":[128],"Process":[129],"(MDP)":[130],"formulation":[131],"derive":[133],"model-based":[135],"upper":[136],"bound":[137],"so":[138],"as":[139],"quantify":[141],"performance":[143,176,179],"gap":[144],"any":[146],"designed":[147],"schemes.":[148],"then":[150],"utilize":[151],"reinforcement":[152],"learning":[153],"(RL)":[154],"design":[156],"R-learning-based":[158],"[3]\u2013[5]":[159],"scheme,":[162],"called":[163],"TSRA.":[164],"carry":[166],"out":[167],"extensive":[168],"simulations":[169],"show":[171],"TSRA":[173],"achieves":[174],"close-to-upper-bound":[175],"than":[180],"existing":[182],"baseline":[183],"DLMA":[184],"[6],":[185],"which":[186],"our":[188],"counterpart":[189],"delay-unconstrained":[192]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
