{"id":"https://openalex.org/W7092208023","doi":"https://doi.org/10.1109/access.2025.3622329","title":"Qualitative Prediction of End-to-End Delay in 5G Networks","display_name":"Qualitative Prediction of End-to-End Delay in 5G Networks","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W7092208023","doi":"https://doi.org/10.1109/access.2025.3622329"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3622329","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3622329","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3622329","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Diyar Fadhil","orcid":"https://orcid.org/0000-0001-8446-1557"},"institutions":[{"id":"https://openalex.org/I83558840","display_name":"Universidade Nova de Lisboa","ror":"https://ror.org/02xankh89","country_code":"PT","type":"education","lineage":["https://openalex.org/I83558840"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Diyar Fadhil","raw_affiliation_strings":["Departamento de Engenharia Electrotécnica e de Computadores, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, Costa da Caparica, Portugal"],"raw_orcid":"https://orcid.org/0000-0001-8446-1557","affiliations":[{"raw_affiliation_string":"Departamento de Engenharia Electrotécnica e de Computadores, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, Costa da Caparica, Portugal","institution_ids":["https://openalex.org/I83558840"]}]},{"author_position":"last","author":{"id":null,"display_name":"Rodolfo Oliveira","orcid":"https://orcid.org/0000-0001-9181-8438"},"institutions":[{"id":"https://openalex.org/I83558840","display_name":"Universidade Nova de Lisboa","ror":"https://ror.org/02xankh89","country_code":"PT","type":"education","lineage":["https://openalex.org/I83558840"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Rodolfo Oliveira","raw_affiliation_strings":["Departamento de Engenharia Electrotécnica e de Computadores, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, Costa da Caparica, Portugal"],"raw_orcid":"https://orcid.org/0000-0001-9181-8438","affiliations":[{"raw_affiliation_string":"Departamento de Engenharia Electrotécnica e de Computadores, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, Costa da Caparica, Portugal","institution_ids":["https://openalex.org/I83558840"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I83558840"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.62926805,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"179406","last_page":"179418"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10184","display_name":"Plant Molecular Biology Research","score":0.5550000071525574,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10184","display_name":"Plant Molecular Biology Research","score":0.5550000071525574,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10869","display_name":"Polysaccharides and Plant Cell Walls","score":0.07209999859333038,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10934","display_name":"Heat shock proteins research","score":0.07010000199079514,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/dynamic-bayesian-network","display_name":"Dynamic Bayesian network","score":0.6786999702453613},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.6758999824523926},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6144000291824341},{"id":"https://openalex.org/keywords/variable-order-bayesian-network","display_name":"Variable-order Bayesian network","score":0.6093000173568726},{"id":"https://openalex.org/keywords/viterbi-algorithm","display_name":"Viterbi algorithm","score":0.5278000235557556},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.5264999866485596},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4896000027656555},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4226999878883362},{"id":"https://openalex.org/keywords/quality-of-service","display_name":"Quality of service","score":0.3580000102519989}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8493000268936157},{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.6786999702453613},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.6758999824523926},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6144000291824341},{"id":"https://openalex.org/C71983512","wikidata":"https://www.wikidata.org/wiki/Q7915687","display_name":"Variable-order Bayesian network","level":4,"score":0.6093000173568726},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6061999797821045},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5911999940872192},{"id":"https://openalex.org/C60582962","wikidata":"https://www.wikidata.org/wiki/Q83886","display_name":"Viterbi algorithm","level":3,"score":0.5278000235557556},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.5264999866485596},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4896000027656555},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4226999878883362},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3905999958515167},{"id":"https://openalex.org/C5119721","wikidata":"https://www.wikidata.org/wiki/Q220501","display_name":"Quality of service","level":2,"score":0.3580000102519989},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.3531999886035919},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.335999995470047},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.32120001316070557},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.305400013923645},{"id":"https://openalex.org/C127043819","wikidata":"https://www.wikidata.org/wiki/Q16243608","display_name":"Bayesian programming","level":5,"score":0.2948000133037567},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C123867240","wikidata":"https://www.wikidata.org/wiki/Q3001792","display_name":"Markov blanket","level":5,"score":0.263700008392334},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.25209999084472656}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3622329","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3622329","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:5416f13419694e099c117eb973004cfc","is_oa":true,"landing_page_url":"https://doaj.org/article/5416f13419694e099c117eb973004cfc","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 179406-179418 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3622329","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3622329","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3375535518","display_name":"Instituto de Telecomunica\u00e7\u00f5es","funder_award_id":"UIDB/50008/2020","funder_id":"https://openalex.org/F4320334779","funder_display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia"},{"id":"https://openalex.org/G4040847614","display_name":"Distributed Access Design for Cell-less Smart 6G Networks","funder_award_id":"2022.08786.PTDC","funder_id":"https://openalex.org/F4320334779","funder_display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia"},{"id":"https://openalex.org/G546278981","display_name":"Instituto de Telecomunica\u00e7\u00f5es","funder_award_id":"UID/50008/2025","funder_id":"https://openalex.org/F4320334779","funder_display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia"},{"id":"https://openalex.org/G7934963516","display_name":null,"funder_award_id":"2022.08786.PTDC","funder_id":"https://openalex.org/F4320328988","funder_display_name":"Instituto de Telecomunica\u00e7\u00f5es"}],"funders":[{"id":"https://openalex.org/F4320328988","display_name":"Instituto de Telecomunica\u00e7\u00f5es","ror":"https://ror.org/02ht4fk33"},{"id":"https://openalex.org/F4320334779","display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","ror":"https://ror.org/00snfqn58"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"end-to-end":[1],"(E2E)":[2],"delay":[3,26,58,95,199],"prediction":[4,27,114,126],"is":[5,100],"critical":[6],"for":[7,194],"optimizing":[8],"network":[9,109,162],"performance":[10],"and":[11,39,64,124,161],"ensuring":[12],"quality":[13],"of":[14,57,121,197],"service":[15],"in":[16,92,148,152,200],"5G":[17,104,201],"networks.":[18,49,202],"This":[19],"paper":[20],"investigates":[21],"two":[22],"complementary":[23],"qualitative":[24],"E2E":[25,94,198],"methodologies:":[28],"a":[29,34,40,69],"Bayesian":[30,51,70,130,143,178],"approach":[31,43,179],"based":[32],"on":[33,117],"Hidden":[35],"Markov":[36],"Model":[37],"(HMM)":[38],"deep":[41],"learning":[42,149],"using":[44,102],"Long":[45],"Short-Term":[46],"Memory":[47],"(LSTM)":[48],"The":[50],"framework":[52],"exploits":[53],"the":[54,74,79,87,93,103,119,125,129,138,142,153,172,177,185],"sequential":[55],"nature":[56],"data":[59,123],"to":[60,167],"infer":[61],"probabilistic":[62,183],"relationships":[63],"estimate":[65],"future":[66],"delays":[67],"via":[68],"chain":[71],"constructed":[72],"with":[73,128],"Viterbi":[75],"algorithm.":[76],"In":[77],"contrast,":[78],"LSTM":[80,139,186],"model":[81],"directly":[82],"learns":[83],"temporal":[84],"dependencies":[85],"from":[86],"data,":[88],"capturing":[89],"complex":[90],"interrelations":[91],"series.":[96],"A":[97],"comprehensive":[98],"evaluation":[99],"conducted":[101],"Campus":[105],"dataset,":[106],"covering":[107],"multiple":[108],"scenarios.":[110],"Results":[111],"show":[112],"that":[113,175],"accuracy":[115],"depends":[116],"both":[118],"amount":[120],"prior":[122],"horizon,":[127],"method":[131],"achieving":[132],"30\u201392%":[133],"accuracy.":[134],"Across":[135],"all":[136],"scenarios,":[137],"consistently":[140],"outperforms":[141],"approach,":[144],"highlighting":[145],"its":[146],"effectiveness":[147],"dynamic":[150],"patterns":[151],"data.":[154],"Scenario-dependent":[155],"factors,":[156],"such":[157],"as":[158],"probing":[159],"rate":[160],"configuration,":[163],"are":[164],"also":[165],"shown":[166],"significantly":[168],"influence":[169],"performance.":[170],"Overall,":[171],"comparison":[173],"demonstrates":[174],"while":[176],"provides":[180],"an":[181],"interpretable":[182],"framework,":[184],"offers":[187],"superior":[188],"predictive":[189],"performance,":[190],"making":[191],"it":[192],"valuable":[193],"proactive":[195],"management":[196]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-17T00:00:00"}