{"id":"https://openalex.org/W2907637692","doi":"https://doi.org/10.1109/pimrc.2018.8581000","title":"Mobile Traffic Prediction from Raw Data Using LSTM Networks","display_name":"Mobile Traffic Prediction from Raw Data Using LSTM Networks","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2907637692","doi":"https://doi.org/10.1109/pimrc.2018.8581000","mag":"2907637692"},"language":"en","primary_location":{"id":"doi:10.1109/pimrc.2018.8581000","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pimrc.2018.8581000","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://zenodo.org/record/2531798","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000099875","display_name":"Hoang Duy Trinh","orcid":"https://orcid.org/0000-0001-5511-6957"},"institutions":[{"id":"https://openalex.org/I4210087295","display_name":"Centre Tecnologic de Telecomunicacions de Catalunya","ror":"https://ror.org/001cwea56","country_code":"ES","type":"nonprofit","lineage":["https://openalex.org/I4210087295","https://openalex.org/I4387153040"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Hoang Duy Trinh","raw_affiliation_strings":["CTTC/CERCA, Castelldefels, Barcelona, Spain"],"affiliations":[{"raw_affiliation_string":"CTTC/CERCA, Castelldefels, Barcelona, Spain","institution_ids":["https://openalex.org/I4210087295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046133708","display_name":"Lorenza Giupponi","orcid":"https://orcid.org/0000-0003-3321-4761"},"institutions":[{"id":"https://openalex.org/I4210087295","display_name":"Centre Tecnologic de Telecomunicacions de Catalunya","ror":"https://ror.org/001cwea56","country_code":"ES","type":"nonprofit","lineage":["https://openalex.org/I4210087295","https://openalex.org/I4387153040"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Lorenza Giupponi","raw_affiliation_strings":["CTTC/CERCA, Castelldefels, Barcelona, Spain"],"affiliations":[{"raw_affiliation_string":"CTTC/CERCA, Castelldefels, Barcelona, Spain","institution_ids":["https://openalex.org/I4210087295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055526140","display_name":"Paolo Dini","orcid":"https://orcid.org/0000-0001-6756-0289"},"institutions":[{"id":"https://openalex.org/I4210087295","display_name":"Centre Tecnologic de Telecomunicacions de Catalunya","ror":"https://ror.org/001cwea56","country_code":"ES","type":"nonprofit","lineage":["https://openalex.org/I4210087295","https://openalex.org/I4387153040"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Paolo Dini","raw_affiliation_strings":["CTTC/CERCA, Castelldefels, Barcelona, Spain"],"affiliations":[{"raw_affiliation_string":"CTTC/CERCA, Castelldefels, Barcelona, Spain","institution_ids":["https://openalex.org/I4210087295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5000099875"],"corresponding_institution_ids":["https://openalex.org/I4210087295"],"apc_list":null,"apc_paid":null,"fwci":8.7663,"has_fulltext":false,"cited_by_count":188,"citation_normalized_percentile":{"value":0.98249312,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1827","last_page":"1832"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.9937000274658203,"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/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.9937000274658203,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.9883999824523926,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12146","display_name":"Power Line Communications and Noise","score":0.9866999983787537,"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/enodeb","display_name":"EnodeB","score":0.8244880437850952},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7765999436378479},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5652673244476318},{"id":"https://openalex.org/keywords/base-station","display_name":"Base station","score":0.5501266121864319},{"id":"https://openalex.org/keywords/telecommunications-link","display_name":"Telecommunications link","score":0.5086055994033813},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.4945342242717743},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.48225122690200806},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.48066961765289307},{"id":"https://openalex.org/keywords/cellular-network","display_name":"Cellular network","score":0.47961127758026123},{"id":"https://openalex.org/keywords/control-channel","display_name":"Control channel","score":0.47571736574172974},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.442417711019516},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.43204420804977417},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3884349763393402},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.3564322590827942},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34600183367729187},{"id":"https://openalex.org/keywords/user-equipment","display_name":"User equipment","score":0.2404598891735077}],"concepts":[{"id":"https://openalex.org/C2776384992","wikidata":"https://www.wikidata.org/wiki/Q5379353","display_name":"EnodeB","level":4,"score":0.8244880437850952},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7765999436378479},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5652673244476318},{"id":"https://openalex.org/C68649174","wikidata":"https://www.wikidata.org/wiki/Q1379116","display_name":"Base station","level":2,"score":0.5501266121864319},{"id":"https://openalex.org/C138660444","wikidata":"https://www.wikidata.org/wiki/Q5607897","display_name":"Telecommunications link","level":2,"score":0.5086055994033813},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4945342242717743},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.48225122690200806},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.48066961765289307},{"id":"https://openalex.org/C153646914","wikidata":"https://www.wikidata.org/wiki/Q535695","display_name":"Cellular network","level":2,"score":0.47961127758026123},{"id":"https://openalex.org/C157607044","wikidata":"https://www.wikidata.org/wiki/Q5165851","display_name":"Control channel","level":3,"score":0.47571736574172974},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.442417711019516},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.43204420804977417},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3884349763393402},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.3564322590827942},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34600183367729187},{"id":"https://openalex.org/C2781327853","wikidata":"https://www.wikidata.org/wiki/Q3552547","display_name":"User equipment","level":3,"score":0.2404598891735077},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/pimrc.2018.8581000","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pimrc.2018.8581000","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","raw_type":"proceedings-article"},{"id":"pmh:oai:zenodo.org:2531798","is_oa":true,"landing_page_url":"https://zenodo.org/record/2531798","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferencePaper"}],"best_oa_location":{"id":"pmh:oai:zenodo.org:2531798","is_oa":true,"landing_page_url":"https://zenodo.org/record/2531798","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferencePaper"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1578473173","https://openalex.org/W1598519330","https://openalex.org/W2054692642","https://openalex.org/W2064675550","https://openalex.org/W2116261113","https://openalex.org/W2117014758","https://openalex.org/W2152175008","https://openalex.org/W2157331557","https://openalex.org/W2412816217","https://openalex.org/W2755551852","https://openalex.org/W2762605243","https://openalex.org/W2964121744","https://openalex.org/W4294177484"],"related_works":["https://openalex.org/W2554819140","https://openalex.org/W2088330545","https://openalex.org/W2027451235","https://openalex.org/W2181435141","https://openalex.org/W1992397565","https://openalex.org/W1494567695","https://openalex.org/W2543647513","https://openalex.org/W2076954211","https://openalex.org/W3082636809","https://openalex.org/W2739738982"],"abstract_inverted_index":{"Predictive":[0],"analysis":[1],"on":[2],"mobile":[3,36,57,172],"network":[4,222],"traffic":[5,37,50,58,233],"is":[6,60,177],"becoming":[7],"of":[8,38,69,123,170,200,210,219],"fundamental":[9],"importance":[10],"for":[11,25,48,149,207,230],"the":[12,19,23,35,49,63,70,73,88,96,102,105,110,119,124,163,171,175,180,184,188,192,196,201,208,211,216,220],"next":[13],"generation":[14],"cellular":[15],"network.":[16],"Proactively":[17],"knowing":[18],"user":[20,116],"demands,":[21],"allows":[22],"system":[24,47,126],"an":[26,39],"optimal":[27],"resource":[28,103],"allocation.":[29],"In":[30,158],"this":[31,80],"paper,":[32],"we":[33,44,82,161],"study":[34],"LTE":[40,71],"base":[41,97],"station":[42],"and":[43,109,195,223],"design":[45,122],"a":[46,136,166,231],"prediction":[51,125,169,181,194,198],"using":[52,72],"Recurrent":[53],"Neural":[54],"Networks.":[55],"The":[56,121],"information":[59,90,100,185,226],"gathered":[61],"from":[62,95,187],"Physical":[64],"Downlink":[65],"Control":[66],"CHannel":[67],"(PDCCH)":[68],"passive":[74],"tool":[75,81],"presented":[76],"in":[77],"[1].":[78],"Using":[79],"are":[83,147],"able":[84],"to":[85,114,118,135,178],"collect":[86],"all":[87],"control":[89],"at":[91],"1":[92],"ms":[93],"resolution":[94],"station.":[98],"This":[99],"comprises":[101],"blocks,":[104],"transport":[106],"block":[107],"size":[108],"modulation":[111],"scheme":[112],"assigned":[113],"each":[115],"connected":[117],"eNodeB.":[120],"includes":[127],"long":[128],"short":[129],"term":[130],"memory":[131,217],"units.":[132],"With":[133],"respect":[134],"Multilayer":[137],"Perceptron":[138],"Network,":[139],"or":[140],"other":[141],"artificial":[142],"neurons":[143],"structures,":[144],"recurrent":[145],"networks":[146],"advantageous":[148],"problems":[150],"with":[151],"sequential":[152],"data":[153],"(e.g.":[154],"language":[155],"modeling)":[156],"[2].":[157],"our":[159],"case,":[160],"state":[162],"problem":[164],"as":[165],"supervised":[167],"multivariate":[168],"traffic,":[173],"where":[174],"objective":[176],"minimize":[179],"error":[182],"given":[183],"extracted":[186],"PDCCH.":[189],"We":[190],"evaluate":[191],"one-step":[193],"long-term":[197],"errors":[199],"proposed":[202],"methodology,":[203],"considering":[204],"different":[205],"numbers":[206],"duration":[209],"observed":[212],"values,":[213],"which":[214],"determines":[215],"length":[218],"LSTM":[221],"how":[224],"much":[225],"must":[227],"be":[228],"stored":[229],"precise":[232],"prediction.":[234]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":34},{"year":2024,"cited_by_count":26},{"year":2023,"cited_by_count":33},{"year":2022,"cited_by_count":23},{"year":2021,"cited_by_count":34},{"year":2020,"cited_by_count":24},{"year":2019,"cited_by_count":9}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}