{"id":"https://openalex.org/W3046072278","doi":"https://doi.org/10.1109/icc40277.2020.9148738","title":"Cellular Traffic Load Prediction with LSTM and Gaussian Process Regression","display_name":"Cellular Traffic Load Prediction with LSTM and Gaussian Process Regression","publication_year":2020,"publication_date":"2020-06-01","ids":{"openalex":"https://openalex.org/W3046072278","doi":"https://doi.org/10.1109/icc40277.2020.9148738","mag":"3046072278"},"language":"en","primary_location":{"id":"doi:10.1109/icc40277.2020.9148738","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc40277.2020.9148738","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100389194","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0003-4845-3466"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039199386","display_name":"Conghao Zhou","orcid":"https://orcid.org/0000-0002-5727-2432"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Conghao Zhou","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Waterloo, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101425444","display_name":"Hongli He","orcid":"https://orcid.org/0000-0002-1283-2168"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongli He","raw_affiliation_strings":["School of Information Engineering, Zhejiang University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Zhejiang University, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074854979","display_name":"Wen Wu","orcid":"https://orcid.org/0000-0002-0458-1282"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Wen Wu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Waterloo, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061723765","display_name":"Weihua Zhuang","orcid":"https://orcid.org/0000-0003-0488-511X"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Weihua Zhuang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Waterloo, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100773343","display_name":"Xuemin Shen","orcid":"https://orcid.org/0000-0002-4140-287X"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Xuemin Shen","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Waterloo, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":14.2035,"has_fulltext":false,"cited_by_count":66,"citation_normalized_percentile":{"value":0.99514673,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9761000275611877,"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/computer-science","display_name":"Computer science","score":0.765089750289917},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7262680530548096},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.6092524528503418},{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.5802702903747559},{"id":"https://openalex.org/keywords/cellular-network","display_name":"Cellular network","score":0.5623617172241211},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.554359495639801},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5334347486495972},{"id":"https://openalex.org/keywords/ground-penetrating-radar","display_name":"Ground-penetrating radar","score":0.5310224890708923},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5208914279937744},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4750024080276489},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.46740227937698364},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.4258750081062317},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42443668842315674},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.41840240359306335},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.21623805165290833},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.11110690236091614},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10807487368583679},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09610629081726074}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.765089750289917},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7262680530548096},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6092524528503418},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.5802702903747559},{"id":"https://openalex.org/C153646914","wikidata":"https://www.wikidata.org/wiki/Q535695","display_name":"Cellular network","level":2,"score":0.5623617172241211},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.554359495639801},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5334347486495972},{"id":"https://openalex.org/C71813955","wikidata":"https://www.wikidata.org/wiki/Q503560","display_name":"Ground-penetrating radar","level":3,"score":0.5310224890708923},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5208914279937744},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4750024080276489},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.46740227937698364},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.4258750081062317},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42443668842315674},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.41840240359306335},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.21623805165290833},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.11110690236091614},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10807487368583679},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09610629081726074},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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":1,"locations":[{"id":"doi:10.1109/icc40277.2020.9148738","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc40277.2020.9148738","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.44999998807907104}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W28713011","https://openalex.org/W1502922572","https://openalex.org/W1984969638","https://openalex.org/W1999479532","https://openalex.org/W2036785686","https://openalex.org/W2062840500","https://openalex.org/W2100649405","https://openalex.org/W2117014758","https://openalex.org/W2528824243","https://openalex.org/W2542136887","https://openalex.org/W2560522919","https://openalex.org/W2762605243","https://openalex.org/W2891286892","https://openalex.org/W2896827527","https://openalex.org/W2921319277","https://openalex.org/W2948063298","https://openalex.org/W2980815094","https://openalex.org/W2999562137","https://openalex.org/W3013430759","https://openalex.org/W4301360474","https://openalex.org/W6629804754"],"related_works":["https://openalex.org/W566010457","https://openalex.org/W2600092203","https://openalex.org/W4293503520","https://openalex.org/W4300066510","https://openalex.org/W2056958800","https://openalex.org/W2803685231","https://openalex.org/W3134152097","https://openalex.org/W4311388919","https://openalex.org/W2966696655","https://openalex.org/W2115519811"],"abstract_inverted_index":{"Accurate":[0],"cellular":[1,65,72,86],"traffic":[2,66,73,144],"load":[3],"prediction":[4,41,105],"is":[5,26,108,125],"a":[6,138],"pre-requisite":[7],"for":[8,137],"efficient":[9],"and":[10,14,23,55,90,123,142],"automatic":[11],"network":[12,32],"planning":[13],"management.":[15],"Considering":[16],"diverse":[17],"users'":[18],"activities":[19],"at":[20,35],"different":[21,36],"locations":[22],"times,":[24],"it":[25,124],"technically":[27],"challenging":[28],"to":[29,48,60,97,110],"characterize":[30],"the":[31,50,69,80,85,92,98,104,112,121,128,133],"resource":[33],"demands":[34],"time":[37,141],"scales":[38],"via":[39],"traditional":[40],"methods.":[42],"In":[43],"this":[44],"paper,":[45],"we":[46],"propose":[47],"combine":[49],"long":[51,140],"short-term":[52],"memory":[53],"(LSTM)":[54],"Gaussian":[56],"process":[57],"regression":[58],"(GPR)":[59],"achieve":[61],"accurate":[62],"single-cell":[63],"level":[64],"prediction,":[67],"using":[68],"open":[70],"Milan":[71],"dataset":[74],"provided":[75],"by":[76],"Telecom":[77],"Italia.":[78],"Firstly,":[79],"dominant":[81],"periodic":[82],"components":[83,94],"of":[84],"data":[87],"are":[88,95,117],"extracted,":[89],"then":[91],"small":[93],"fed":[96],"LSTM":[99],"network.":[100],"To":[101],"further":[102],"improve":[103],"accuracy,":[106],"GPR":[107],"used":[109],"recover":[111],"residual":[113],"components.":[114],"Extensive":[115],"experiments":[116],"conducted":[118],"based":[119],"on":[120],"dataset,":[122],"shown":[126],"that":[127],"proposed":[129],"LSTM-GPR":[130],"scheme":[131],"outperforms":[132],"benchmark":[134],"schemes,":[135],"especially":[136],"relatively":[139],"burst":[143],"prediction.":[145]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
