{"id":"https://openalex.org/W4284969930","doi":"https://doi.org/10.1109/eucnc/6gsummit54941.2022.9815831","title":"Incorporation of Confidence Interval into Rate Selection Based on the Extreme Value Theory for Ultra-Reliable Communications","display_name":"Incorporation of Confidence Interval into Rate Selection Based on the Extreme Value Theory for Ultra-Reliable Communications","publication_year":2022,"publication_date":"2022-06-07","ids":{"openalex":"https://openalex.org/W4284969930","doi":"https://doi.org/10.1109/eucnc/6gsummit54941.2022.9815831"},"language":"en","primary_location":{"id":"doi:10.1109/eucnc/6gsummit54941.2022.9815831","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eucnc/6gsummit54941.2022.9815831","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 Joint European Conference on Networks and Communications &amp; 6G Summit (EuCNC/6G Summit)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2401.05888","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073892349","display_name":"Niloofar Mehrnia","orcid":"https://orcid.org/0000-0002-5475-2238"},"institutions":[{"id":"https://openalex.org/I1351752","display_name":"Ko\u00e7 University","ror":"https://ror.org/00jzwgz36","country_code":"TR","type":"education","lineage":["https://openalex.org/I1351752"]},{"id":"https://openalex.org/I4210154947","display_name":"Ford Otosan (Turkey)","ror":"https://ror.org/04h48jk55","country_code":"TR","type":"company","lineage":["https://openalex.org/I4210154947"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Niloofar Mehrnia","raw_affiliation_strings":["Koc University Ford Otosan Automotive Technologies Laboratory (KUFOTAL), Sariyer,Istanbul,Turkey,34450"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Koc University Ford Otosan Automotive Technologies Laboratory (KUFOTAL), Sariyer,Istanbul,Turkey,34450","institution_ids":["https://openalex.org/I1351752","https://openalex.org/I4210154947"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063874470","display_name":"Sinem \u00c7\u00f6leri","orcid":"https://orcid.org/0000-0002-7502-3122"},"institutions":[{"id":"https://openalex.org/I1351752","display_name":"Ko\u00e7 University","ror":"https://ror.org/00jzwgz36","country_code":"TR","type":"education","lineage":["https://openalex.org/I1351752"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Sinem Coleri","raw_affiliation_strings":["Koc University,Department of Electrical and Electronics Engineering,Istanbul,Turkey,34450"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Koc University,Department of Electrical and Electronics Engineering,Istanbul,Turkey,34450","institution_ids":["https://openalex.org/I1351752"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9282,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.74922566,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"118","last_page":"123"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9919000267982483,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9890000224113464,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/extrapolation","display_name":"Extrapolation","score":0.6852877140045166},{"id":"https://openalex.org/keywords/extreme-value-theory","display_name":"Extreme value theory","score":0.667361319065094},{"id":"https://openalex.org/keywords/confidence-interval","display_name":"Confidence interval","score":0.6500365138053894},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5311461687088013},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5014195442199707},{"id":"https://openalex.org/keywords/transmission","display_name":"Transmission (telecommunications)","score":0.4976404011249542},{"id":"https://openalex.org/keywords/transmission-rate","display_name":"Transmission rate","score":0.46404120326042175},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.4446229338645935},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4316532015800476},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3749786615371704},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3418259918689728},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29748430848121643},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.10120591521263123},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.09728935360908508}],"concepts":[{"id":"https://openalex.org/C132459708","wikidata":"https://www.wikidata.org/wiki/Q744069","display_name":"Extrapolation","level":2,"score":0.6852877140045166},{"id":"https://openalex.org/C147581598","wikidata":"https://www.wikidata.org/wiki/Q729429","display_name":"Extreme value theory","level":2,"score":0.667361319065094},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.6500365138053894},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5311461687088013},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5014195442199707},{"id":"https://openalex.org/C761482","wikidata":"https://www.wikidata.org/wiki/Q118093","display_name":"Transmission (telecommunications)","level":2,"score":0.4976404011249542},{"id":"https://openalex.org/C2989335485","wikidata":"https://www.wikidata.org/wiki/Q17130189","display_name":"Transmission rate","level":3,"score":0.46404120326042175},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.4446229338645935},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4316532015800476},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3749786615371704},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3418259918689728},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29748430848121643},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.10120591521263123},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.09728935360908508},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/eucnc/6gsummit54941.2022.9815831","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eucnc/6gsummit54941.2022.9815831","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 Joint European Conference on Networks and Communications &amp; 6G Summit (EuCNC/6G Summit)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2401.05888","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.05888","pdf_url":"https://arxiv.org/pdf/2401.05888","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:cdm21054.contentdm.oclc.org:IR/10686","is_oa":false,"landing_page_url":"https://doi.org/10.1109/EuCNC/6GSummit54941.2022.9815831","pdf_url":null,"source":{"id":"https://openalex.org/S4306401342","display_name":"Digital Collections portal (Ko\u00e7 University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1351752","host_organization_name":"Ko\u00e7 University","host_organization_lineage":["https://openalex.org/I1351752"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2022 Joint European Conference on Networks and Communications and 6G Summit, EuCNC/6G Summit 2022","raw_type":"Conference proceeding"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2401.05888","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.05888","pdf_url":"https://arxiv.org/pdf/2401.05888","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4284969930.pdf"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W1500657154","https://openalex.org/W2134186144","https://openalex.org/W2171884936","https://openalex.org/W2611265312","https://openalex.org/W2776794349","https://openalex.org/W2890858422","https://openalex.org/W2963250023","https://openalex.org/W2963599038","https://openalex.org/W3105989638","https://openalex.org/W3115084678","https://openalex.org/W3173958306","https://openalex.org/W3180768641","https://openalex.org/W3187955526","https://openalex.org/W4226032091","https://openalex.org/W4239656095","https://openalex.org/W4287201816","https://openalex.org/W4287871747","https://openalex.org/W4298193418","https://openalex.org/W6773761408"],"related_works":["https://openalex.org/W2220129715","https://openalex.org/W2014541560","https://openalex.org/W4303188256","https://openalex.org/W2057373456","https://openalex.org/W2308693247","https://openalex.org/W2117046038","https://openalex.org/W2083725625","https://openalex.org/W268961470","https://openalex.org/W2359342237","https://openalex.org/W4327813467"],"abstract_inverted_index":{"Proper":[0],"determination":[1],"of":[2,28,67,71,111,118,141,154],"the":[3,20,25,44,51,69,76,82,86,95,104,108,116,119,124,128,132,142,147,158],"transmission":[4,52,96,143],"rate":[5,53,97,121,144],"in":[6],"ultra-reliable":[7,61],"low":[8],"latency":[9],"communication":[10,62],"(URLLC)":[11],"needs":[12],"to":[13,24,81,157],"incorporate":[14],"a":[15,40,99,151],"confidence":[16,100,129],"interval":[17,130],"(CI)":[18],"for":[19,31,49,59,131],"estimated":[21,120],"parameters":[22,88],"due":[23],"large":[26],"amount":[27],"data":[29,105],"required":[30],"their":[32,90],"accurate":[33],"estimation.":[34],"In":[35],"this":[36],"paper,":[37],"we":[38,114,136],"propose":[39],"framework":[41,65,126,149],"based":[42,145],"on":[43,103,146],"extreme":[45,72],"value":[46],"theory":[47],"(EVT)":[48],"determining":[50],"along":[54],"with":[55],"its":[56],"corresponding":[57],"CI":[58],"an":[60],"system.":[63],"This":[64],"consists":[66],"characterizing":[68],"statistics":[70],"events":[73],"by":[74],"fitting":[75],"generalized":[77],"Pareto":[78],"distribution":[79],"(GPD)":[80],"channel":[83],"tail,":[84],"deriving":[85],"GPD":[87,133],"and":[89,93],"associated":[91],"CIs,":[92],"obtaining":[94],"within":[98,107],"interval.":[101],"Based":[102],"collected":[106],"engine":[109],"compartment":[110],"Fiat":[112],"Linea,":[113],"demonstrate":[115],"accuracy":[117],"obtained":[122],"through":[123],"EVT-based":[125],"considering":[127],"parameters.":[134],"Additionally,":[135],"show":[137],"that":[138],"proper":[139],"estimation":[140],"proposed":[148],"requires":[150],"lower":[152],"number":[153],"samples":[155],"compared":[156],"traditional":[159],"extrapolation-based":[160],"approaches.":[161]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-10T00:00:00"}
