{"id":"https://openalex.org/W4383473466","doi":"https://doi.org/10.48550/arxiv.2307.02329","title":"Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements","display_name":"Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements","publication_year":2023,"publication_date":"2023-07-05","ids":{"openalex":"https://openalex.org/W4383473466","doi":"https://doi.org/10.48550/arxiv.2307.02329"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2307.02329","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.02329","pdf_url":"https://arxiv.org/pdf/2307.02329","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2307.02329","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029620038","display_name":"Marco Skocaj","orcid":"https://orcid.org/0000-0001-9172-0422"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Skocaj, Marco","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103055424","display_name":"Francesca Conserva","orcid":"https://orcid.org/0000-0001-5337-5752"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Conserva, Francesca","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092418579","display_name":"Nicol Sarcone Grande","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Grande, Nicol Sarcone","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069676059","display_name":"Andrea Orsi","orcid":"https://orcid.org/0000-0002-2433-9610"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Orsi, Andrea","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018709438","display_name":"Davide Micheli","orcid":"https://orcid.org/0000-0002-7851-0532"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Micheli, Davide","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021006110","display_name":"Giorgio Ghinamo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ghinamo, Giorgio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007841522","display_name":"Simone Bizzarri","orcid":"https://orcid.org/0000-0002-5700-0621"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bizzarri, Simone","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5022214905","display_name":"Roberto Verdone","orcid":"https://orcid.org/0000-0001-6522-8573"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Verdone, Roberto","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":true,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.983299970626831,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.983299970626831,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7550740838050842},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.674435019493103},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5778310894966125},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5728319883346558},{"id":"https://openalex.org/keywords/cellular-network","display_name":"Cellular network","score":0.5672978758811951},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.5363043546676636},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5107977390289307},{"id":"https://openalex.org/keywords/predictive-analytics","display_name":"Predictive analytics","score":0.510209321975708},{"id":"https://openalex.org/keywords/quality-of-service","display_name":"Quality of service","score":0.4944002628326416},{"id":"https://openalex.org/keywords/mobile-broadband","display_name":"Mobile broadband","score":0.49021559953689575},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.4364078640937805},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4162033200263977},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3927414119243622},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.21202436089515686},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.10882055759429932},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09504148364067078}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7550740838050842},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.674435019493103},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5778310894966125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5728319883346558},{"id":"https://openalex.org/C153646914","wikidata":"https://www.wikidata.org/wiki/Q535695","display_name":"Cellular network","level":2,"score":0.5672978758811951},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.5363043546676636},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5107977390289307},{"id":"https://openalex.org/C83209312","wikidata":"https://www.wikidata.org/wiki/Q1053367","display_name":"Predictive analytics","level":2,"score":0.510209321975708},{"id":"https://openalex.org/C5119721","wikidata":"https://www.wikidata.org/wiki/Q220501","display_name":"Quality of service","level":2,"score":0.4944002628326416},{"id":"https://openalex.org/C78834623","wikidata":"https://www.wikidata.org/wiki/Q640394","display_name":"Mobile broadband","level":3,"score":0.49021559953689575},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.4364078640937805},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4162033200263977},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3927414119243622},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.21202436089515686},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.10882055759429932},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09504148364067078}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2307.02329","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.02329","pdf_url":"https://arxiv.org/pdf/2307.02329","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2307.02329","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2307.02329","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2307.02329","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.02329","pdf_url":"https://arxiv.org/pdf/2307.02329","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.5199999809265137,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G7205771190","display_name":null,"funder_award_id":"PE00000001","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4383473466.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2105642232","https://openalex.org/W3197833032","https://openalex.org/W3207332793","https://openalex.org/W3124356676","https://openalex.org/W4386081464","https://openalex.org/W2499612753","https://openalex.org/W2986629030","https://openalex.org/W188224805","https://openalex.org/W2968737745","https://openalex.org/W2497162478"],"abstract_inverted_index":{"The":[0,31],"advent":[1],"of":[2,15,33,42,69,82,95,132,149],"novel":[3],"5G":[4,46],"services":[5],"and":[6,12,24,89,100,116,137],"applications":[7],"with":[8,86],"binding":[9],"latency":[10,44,72],"requirements":[11],"guaranteed":[13],"Quality":[14],"Service":[16],"(QoS)":[17],"hastened":[18],"the":[19,70,147],"need":[20],"to":[21,37,56],"incorporate":[22],"autonomous":[23],"proactive":[25],"decision-making":[26],"in":[27,107,152],"network":[28,51,58],"management":[29],"procedures.":[30],"objective":[32],"our":[34,124],"study":[35],"is":[36,54,78],"provide":[38,143],"a":[39,74,83],"thorough":[40],"analysis":[41,85],"predictive":[43,101,125,150],"within":[45],"networks":[47],"by":[48,80],"utilizing":[49],"real-world":[50],"data":[52,128],"that":[53],"accessible":[55],"mobile":[57],"operators":[59],"(MNOs).":[60],"In":[61],"particular,":[62],"(i)":[63],"we":[64,91],"present":[65],"an":[66],"analytical":[67],"formulation":[68],"user-plane":[71],"as":[73,112],"Hypoexponential":[75],"distribution,":[76],"which":[77],"validated":[79],"means":[81],"comparative":[84],"empirical":[87],"measurements,":[88],"(ii)":[90],"conduct":[92],"experimental":[93],"results":[94,142],"probabilistic":[96],"regression,":[97],"anomaly":[98],"detection,":[99],"forecasting":[102],"leveraging":[103],"on":[104,119],"emerging":[105],"domains":[106],"Machine":[108,117],"Learning":[109,114,118],"(ML),":[110],"such":[111],"Bayesian":[113],"(BL)":[115],"Graphs":[120],"(GML).":[121],"We":[122],"test":[123],"framework":[126],"using":[127],"gathered":[129],"from":[130],"scenarios":[131],"vehicular":[133],"mobility,":[134],"dense-urban":[135],"traffic,":[136],"social":[138],"gathering":[139],"events.":[140],"Our":[141],"valuable":[144],"insights":[145],"into":[146],"efficacy":[148],"algorithms":[151],"practical":[153],"applications.":[154]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}
