{"id":"https://openalex.org/W3209187475","doi":"https://doi.org/10.1109/mwc.011.2000344","title":"dmTP: A Deep Meta-Learning Based Framework for Mobile Traffic Prediction","display_name":"dmTP: A Deep Meta-Learning Based Framework for Mobile Traffic Prediction","publication_year":2021,"publication_date":"2021-10-01","ids":{"openalex":"https://openalex.org/W3209187475","doi":"https://doi.org/10.1109/mwc.011.2000344","mag":"3209187475"},"language":"en","primary_location":{"id":"doi:10.1109/mwc.011.2000344","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mwc.011.2000344","pdf_url":null,"source":{"id":"https://openalex.org/S146764194","display_name":"IEEE Wireless Communications","issn_l":"1536-1284","issn":["1536-1284","1558-0687"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Wireless Communications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.1109/MWC.011.2000344","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046428718","display_name":"Zitian Zhang","orcid":"https://orcid.org/0000-0003-1825-735X"},"institutions":[{"id":"https://openalex.org/I4210153300","display_name":"Shanghai Research Center for Wireless Communications","ror":"https://ror.org/04xfd0826","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210153300"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zitian Zhang","raw_affiliation_strings":["Ranplan Wireless Network Design Ltd,China","Ranplan Wireless Network Design Ltd, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ranplan Wireless Network Design Ltd,China","institution_ids":["https://openalex.org/I4210153300"]},{"raw_affiliation_string":"Ranplan Wireless Network Design Ltd, China","institution_ids":["https://openalex.org/I4210153300"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102927253","display_name":"Fuyou Li","orcid":"https://orcid.org/0000-0001-8168-5475"},"institutions":[{"id":"https://openalex.org/I91136226","display_name":"University of Sheffield","ror":"https://ror.org/05krs5044","country_code":"GB","type":"education","lineage":["https://openalex.org/I91136226"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Fuyou Li","raw_affiliation_strings":["University of Sheffield,United Kingdom","University of Sheffield, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Sheffield,United Kingdom","institution_ids":["https://openalex.org/I91136226"]},{"raw_affiliation_string":"University of Sheffield, United Kingdom","institution_ids":["https://openalex.org/I91136226"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069850104","display_name":"Xiaoli Chu","orcid":"https://orcid.org/0000-0003-1863-6149"},"institutions":[{"id":"https://openalex.org/I91136226","display_name":"University of Sheffield","ror":"https://ror.org/05krs5044","country_code":"GB","type":"education","lineage":["https://openalex.org/I91136226"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Xiaoli Chu","raw_affiliation_strings":["University of Sheffield,United Kingdom","University of Sheffield, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Sheffield,United Kingdom","institution_ids":["https://openalex.org/I91136226"]},{"raw_affiliation_string":"University of Sheffield, United Kingdom","institution_ids":["https://openalex.org/I91136226"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016290340","display_name":"Yuguang Fang","orcid":"https://orcid.org/0000-0002-1079-3871"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuguang Fang","raw_affiliation_strings":["University of Florida,USA","University of Florida, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Florida,USA","institution_ids":["https://openalex.org/I33213144"]},{"raw_affiliation_string":"University of Florida, USA","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100436731","display_name":"Jie Zhang","orcid":"https://orcid.org/0000-0002-3354-0690"},"institutions":[{"id":"https://openalex.org/I91136226","display_name":"University of Sheffield","ror":"https://ror.org/05krs5044","country_code":"GB","type":"education","lineage":["https://openalex.org/I91136226"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jie Zhang","raw_affiliation_strings":["University of Sheffield,United Kingdom","University of Sheffield, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Sheffield,United Kingdom","institution_ids":["https://openalex.org/I91136226"]},{"raw_affiliation_string":"University of Sheffield, United Kingdom","institution_ids":["https://openalex.org/I91136226"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8207,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.70691619,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"28","issue":"5","first_page":"110","last_page":"117"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9858999848365784,"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"}},{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9779999852180481,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8550084829330444},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6804482936859131},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6793696284294128},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6704932451248169},{"id":"https://openalex.org/keywords/meta-learning","display_name":"Meta learning (computer science)","score":0.6030733585357666},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5931018590927124},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.547612190246582},{"id":"https://openalex.org/keywords/base-station","display_name":"Base station","score":0.525881290435791},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.45252129435539246},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.4353814721107483},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3485637307167053},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.0760229229927063}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8550084829330444},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6804482936859131},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6793696284294128},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6704932451248169},{"id":"https://openalex.org/C2781002164","wikidata":"https://www.wikidata.org/wiki/Q6822311","display_name":"Meta learning (computer science)","level":3,"score":0.6030733585357666},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5931018590927124},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.547612190246582},{"id":"https://openalex.org/C68649174","wikidata":"https://www.wikidata.org/wiki/Q1379116","display_name":"Base station","level":2,"score":0.525881290435791},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.45252129435539246},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.4353814721107483},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3485637307167053},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0760229229927063},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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/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/mwc.011.2000344","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mwc.011.2000344","pdf_url":null,"source":{"id":"https://openalex.org/S146764194","display_name":"IEEE Wireless Communications","issn_l":"1536-1284","issn":["1536-1284","1558-0687"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Wireless Communications","raw_type":"journal-article"},{"id":"pmh:oai:zenodo.org:6387578","is_oa":true,"landing_page_url":"https://doi.org/10.1109/MWC.011.2000344","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:zenodo.org:6387578","is_oa":true,"landing_page_url":"https://doi.org/10.1109/MWC.011.2000344","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8086738684","display_name":null,"funder_award_id":"778305,843401","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W28713011","https://openalex.org/W1598519330","https://openalex.org/W2126545577","https://openalex.org/W2172064003","https://openalex.org/W2600602093","https://openalex.org/W2612472936","https://openalex.org/W2807536558","https://openalex.org/W2902753153","https://openalex.org/W2919115771","https://openalex.org/W2921319277","https://openalex.org/W2963043696","https://openalex.org/W2963389592","https://openalex.org/W2963601182","https://openalex.org/W3029652632","https://openalex.org/W6635815882","https://openalex.org/W6678654976","https://openalex.org/W6745134051"],"related_works":["https://openalex.org/W1488960379","https://openalex.org/W3004751640","https://openalex.org/W4319075079","https://openalex.org/W4287637345","https://openalex.org/W3093147807","https://openalex.org/W1530810814","https://openalex.org/W4287992200","https://openalex.org/W3105253998","https://openalex.org/W4385484791","https://openalex.org/W4320498382"],"abstract_inverted_index":{"Deep":[0],"learning":[1,15,111],"technologies":[2],"have":[3],"been":[4],"widely":[5],"exploited":[6],"to":[7,32,56,104,121,139,171],"predict":[8],"mobile":[9,46,80],"traffic.":[10],"However,":[11],"individually":[12],"training":[13,129],"deep":[14,43],"models":[16],"for":[17,62,99,131],"various":[18],"traffic":[19,34,47,81],"prediction":[20,48,60,65,73,82,108,156,188],"tasks":[21],"is":[22],"not":[23],"only":[24],"time":[25],"consuming":[26],"but":[27],"also":[28],"unrealistic,":[29],"sometimes":[30],"due":[31],"limited":[33],"records.":[35],"In":[36,75,102],"this":[37],"article,":[38],"we":[39,77,113],"propose":[40],"a":[41,85,93,135,151],"novel":[42],"meta-learning":[44,158],"based":[45],"framework,":[49],"namely,":[50],"dmTP,":[51,76],"which":[52],"can":[53],"adaptively":[54],"learn":[55,57],"the":[58,97,106,119,123,132,160,164,173,186],"proper":[59],"model":[61],"each":[63,79,100],"distinct":[64],"task":[66,83],"from":[67],"accumulated":[68],"meta-knowledge":[69],"of":[70,134,166],"previously":[71],"learned":[72],"tasks.":[74],"regard":[78],"as":[84,96,118,183],"base-task":[86,137],"and":[87,110,127,168,179],"adopt":[88],"an":[89,116],"LSTM":[90],"network":[91],"with":[92,144,185],"fixed":[94],"structure":[95],"base-learner":[98,133],"base-task.":[101],"order":[103],"improve":[105],"base-learner's":[107],"accuracy":[109],"efficiency,":[112],"further":[114],"employ":[115],"MLP":[117],"meta-learner":[120],"find":[122],"optimal":[124],"hyper-parameter":[125],"value":[126],"initial":[128],"status":[130],"new":[136],"according":[138],"its":[140],"meta-features.":[141],"Extensive":[142],"experiments":[143],"real-world":[145],"datasets":[146],"demonstrate":[147],"that":[148],"while":[149],"guaranteeing":[150],"similar":[152],"or":[153],"even":[154],"better":[155],"accuracy,":[157],"in":[159],"proposed":[161],"dmTP":[162],"reduces":[163],"numbers":[165],"epochs":[167],"base-samples":[169],"needed":[170],"train":[172],"base-learners":[174],"by":[175],"around":[176],"75":[177],"percent":[178],"81":[180],"percent,":[181],"respectively,":[182],"compared":[184],"existing":[187],"models.":[189]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}