{"id":"https://openalex.org/W7154509793","doi":"https://doi.org/10.48550/arxiv.2604.12903","title":"Joint Clustering and Prediction of the Quality of Service in Vehicular Cellular Networks","display_name":"Joint Clustering and Prediction of the Quality of Service in Vehicular Cellular Networks","publication_year":2026,"publication_date":"2026-04-14","ids":{"openalex":"https://openalex.org/W7154509793","doi":"https://doi.org/10.48550/arxiv.2604.12903"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.12903","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12903","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.12903","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040913554","display_name":"Oscar Stenhammar","orcid":"https://orcid.org/0000-0001-8499-9162"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stenhammar, Oscar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133667812","display_name":"Gabor Fodor","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fodor, G\u00e1bor","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5077148700","display_name":"Carlo Fischione","orcid":"https://orcid.org/0000-0001-9810-3478"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fischione, Carlo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.5397999882698059,"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/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.5397999882698059,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.07519999891519547,"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"}},{"id":"https://openalex.org/T13553","display_name":"Age of Information Optimization","score":0.06520000100135803,"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/cluster-analysis","display_name":"Cluster analysis","score":0.6517000198364258},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6342999935150146},{"id":"https://openalex.org/keywords/quality-of-service","display_name":"Quality of service","score":0.5108000040054321},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.44699999690055847},{"id":"https://openalex.org/keywords/train","display_name":"Train","score":0.44530001282691956},{"id":"https://openalex.org/keywords/cellular-network","display_name":"Cellular network","score":0.43849998712539673},{"id":"https://openalex.org/keywords/handover","display_name":"Handover","score":0.4277999997138977},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.42239999771118164},{"id":"https://openalex.org/keywords/divide-and-conquer-algorithms","display_name":"Divide and conquer algorithms","score":0.34689998626708984},{"id":"https://openalex.org/keywords/wireless-network","display_name":"Wireless network","score":0.3314000070095062}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.757099986076355},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6517000198364258},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6342999935150146},{"id":"https://openalex.org/C5119721","wikidata":"https://www.wikidata.org/wiki/Q220501","display_name":"Quality of service","level":2,"score":0.5108000040054321},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4724999964237213},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.44699999690055847},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.44530001282691956},{"id":"https://openalex.org/C153646914","wikidata":"https://www.wikidata.org/wiki/Q535695","display_name":"Cellular network","level":2,"score":0.43849998712539673},{"id":"https://openalex.org/C111852164","wikidata":"https://www.wikidata.org/wiki/Q1414679","display_name":"Handover","level":2,"score":0.4277999997138977},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.4237000048160553},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.42239999771118164},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4178999960422516},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38940000534057617},{"id":"https://openalex.org/C71559656","wikidata":"https://www.wikidata.org/wiki/Q671298","display_name":"Divide and conquer algorithms","level":2,"score":0.34689998626708984},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.3314000070095062},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.32010000944137573},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3109999895095825},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.30480000376701355},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.30379998683929443},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C2777115002","wikidata":"https://www.wikidata.org/wiki/Q7168246","display_name":"Performance prediction","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C110157686","wikidata":"https://www.wikidata.org/wiki/Q922122","display_name":"Broadcasting (networking)","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.25940001010894775},{"id":"https://openalex.org/C203274722","wikidata":"https://www.wikidata.org/wiki/Q7001161","display_name":"Network performance","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C126831891","wikidata":"https://www.wikidata.org/wiki/Q221673","display_name":"Host (biology)","level":2,"score":0.2531000077724457},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2524999976158142},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.12903","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12903","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.12903","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12903","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Machine":[0],"learning":[1,33],"models":[2,66,98,117,224],"are":[3],"increasingly":[4],"deployed":[5],"in":[6,159],"wireless":[7],"networks":[8],"with":[9,82,138],"stringent":[10],"performance":[11],"requirements.":[12],"However,":[13],"dynamic":[14],"propagation":[15],"environments":[16],"and":[17,45,73,113,142,151,179,198],"fluctuating":[18],"traffic":[19],"densities":[20],"introduce":[21],"concept":[22,120,164],"drift,":[23,121],"which":[24],"complicates":[25],"the":[26,91,111,115,139,143,148,160,175,191,200,214],"ability":[27],"to":[28,52,89,118,128,157,207],"maintain":[29],"accurate":[30],"predictive":[31,48,116,195],"machine":[32],"models.":[34],"We":[35],"propose":[36],"a":[37,69,75,124,172,182],"distributed":[38,227],"optimization":[39],"framework":[40],"that":[41,79,147,155,162,213],"jointly":[42],"clusters":[43,110],"cells":[44,81,112],"trains":[46],"cluster-level":[47],"models,":[49],"enabling":[50],"nodes":[51],"cooperatively":[53],"predict":[54],"quality":[55],"of":[56,174],"service":[57],"(QoS)":[58],"distributions":[59],"under":[60],"communication":[61],"constraints.":[62],"The":[63,166,186],"proposed":[64,187,215],"method":[65,149,188,216],"QoS":[67],"as":[68],"multivariate":[70],"Gaussian/lognormal":[71],"distribution":[72,176],"uses":[74],"novel":[76],"clustering":[77],"mechanism":[78],"groups":[80],"similar":[83],"network":[84,161],"conditions,":[85],"allowing":[86],"each":[87,100],"cell":[88],"select":[90],"most":[92],"appropriate":[93],"predictor":[94],"without":[95],"retraining":[96],"new":[97],"for":[99],"cell.":[101],"By":[102],"leveraging":[103],"block":[104],"coordinate":[105],"descent,":[106],"our":[107],"solution":[108],"efficiently":[109],"updates":[114],"mitigate":[119],"while":[122],"maintaining":[123],"compact":[125],"model":[126,196],"set":[127],"minimize":[129],"computation":[130],"overhead.":[131],"Evaluation":[132],"using":[133,221],"data":[134],"from":[135],"realistic":[136],"simulations":[137],"Sionna":[140],"ray-tracer":[141],"ns-3":[144],"simulator":[145],"shows":[146],"converges":[150],"yields":[152],"cluster":[153],"constellations":[154],"adapt":[156],"changes":[158],"cause":[163],"drift.":[165],"experimental":[167],"evaluation":[168],"focuses":[169],"on":[170],"providing":[171],"prediction":[173,184],"latency,":[177],"jitter,":[178],"RSRP":[180],"over":[181],"one-hour":[183],"horizon.":[185],"significantly":[189],"outperforms":[190],"traditional":[192],"single":[193],"global":[194],"approach":[197],"reduces":[199],"mean":[201],"absolute":[202],"error":[203],"by":[204],"9-27%":[205],"compared":[206],"local":[208,219],"cell-level":[209],"predictors.":[210],"This":[211],"demonstrates":[212],"effectively":[217],"captures":[218],"variability":[220],"far":[222],"fewer":[223],"through":[225],"scalable":[226],"clustering.":[228]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-16T00:00:00"}