{"id":"https://openalex.org/W4391129856","doi":"https://doi.org/10.1109/ic-nidc59918.2023.10390575","title":"Lotnet: A Low Overhead Approach for Cellular Traffic Prediction of Large-Scale Urban Area","display_name":"Lotnet: A Low Overhead Approach for Cellular Traffic Prediction of Large-Scale Urban Area","publication_year":2023,"publication_date":"2023-11-03","ids":{"openalex":"https://openalex.org/W4391129856","doi":"https://doi.org/10.1109/ic-nidc59918.2023.10390575"},"language":"en","primary_location":{"id":"doi:10.1109/ic-nidc59918.2023.10390575","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ic-nidc59918.2023.10390575","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 8th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","raw_type":"proceedings-article"},"type":"article","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/A5103741486","display_name":"Dongliang Li","orcid":"https://orcid.org/0000-0001-8241-1986"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dongliang Li","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China,100876"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China,100876","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101502584","display_name":"Haozhen Li","orcid":"https://orcid.org/0000-0003-2554-4218"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haozhen Li","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China,100876"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China,100876","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100626319","display_name":"Hui Zhang","orcid":"https://orcid.org/0000-0002-4861-0540"},"institutions":[{"id":"https://openalex.org/I4210144143","display_name":"Inspur (China)","ror":"https://ror.org/0474p4r72","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210144143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Zhang","raw_affiliation_strings":["Shandong Inspur Database Technology Co., Ltd,Jinan,China","Shandong Inspur Database Technology Co., Ltd, Jinan, China"],"affiliations":[{"raw_affiliation_string":"Shandong Inspur Database Technology Co., Ltd,Jinan,China","institution_ids":["https://openalex.org/I4210144143"]},{"raw_affiliation_string":"Shandong Inspur Database Technology Co., Ltd, Jinan, China","institution_ids":["https://openalex.org/I4210144143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014860128","display_name":"Boyuan Zhang","orcid":"https://orcid.org/0000-0002-7831-6318"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Boyuan Zhang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China,100876"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China,100876","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018769767","display_name":"Xinyu Gu","orcid":"https://orcid.org/0000-0001-6762-7463"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]},{"id":"https://openalex.org/I4210155350","display_name":"Purple Mountain Laboratories","ror":"https://ror.org/04zcbk583","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210155350"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyu Gu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China,100876","Purple Mountain Laboratories, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China,100876","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Purple Mountain Laboratories, Nanjing, China","institution_ids":["https://openalex.org/I4210155350"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5103741486"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22279099,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"325","last_page":"330"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9980999827384949,"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/T13918","display_name":"Advanced Data and IoT Technologies","score":0.989300012588501,"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":0.7993292808532715},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.6105305552482605},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.5261330604553223},{"id":"https://openalex.org/keywords/cellular-network","display_name":"Cellular network","score":0.5161646008491516},{"id":"https://openalex.org/keywords/traffic-generation-model","display_name":"Traffic generation model","score":0.48520568013191223},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4472874701023102},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.41387200355529785},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.37283095717430115},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.3500756621360779},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27810245752334595}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7993292808532715},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.6105305552482605},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.5261330604553223},{"id":"https://openalex.org/C153646914","wikidata":"https://www.wikidata.org/wiki/Q535695","display_name":"Cellular network","level":2,"score":0.5161646008491516},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.48520568013191223},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4472874701023102},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.41387200355529785},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.37283095717430115},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3500756621360779},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27810245752334595},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ic-nidc59918.2023.10390575","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ic-nidc59918.2023.10390575","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 8th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8299999833106995,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2102041666","https://openalex.org/W2190432600","https://openalex.org/W2807536558","https://openalex.org/W2884585870","https://openalex.org/W2914304175","https://openalex.org/W2921319277","https://openalex.org/W3206043672","https://openalex.org/W4287331249"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W4392946183","https://openalex.org/W3088732000","https://openalex.org/W2114492868","https://openalex.org/W3085708078","https://openalex.org/W2538283180"],"abstract_inverted_index":{"With":[0],"the":[1,9,12,15,44,55,95,99,113,120,124,132,141,156],"continuous":[2],"development":[3],"of":[4,11,27,47,126,158],"mobile":[5,18],"communication":[6],"technology":[7],"and":[8,36,102,112],"arrival":[10],"5G":[13],"era,":[14],"demand":[16],"for":[17,86,123,144],"data":[19,33,154],"traffic":[20,24,50,58,153],"is":[21,26],"increasing.":[22],"Accurate":[23],"prediction":[25,59,83,92,121],"great":[28],"significance":[29],"in":[30,51,119],"meeting":[31],"users'":[32],"transmission":[34],"needs":[35],"optimizing":[37],"network":[38,49],"resource":[39],"allocation.":[40],"However,":[41],"due":[42],"to":[43,90,130],"uneven":[45],"distribution":[46],"cellular":[48,57,152],"large-scale":[52,96,137],"urban":[53],"area,":[54,98],"current":[56],"methods":[60],"based":[61],"on":[62,150],"deep":[63],"learning":[64],"suffer":[65],"from":[66],"performance":[67,87,93],"degradation":[68],"as":[69,71],"well":[70],"large":[72],"storage":[73,133],"overhead.":[74],"Therefore,":[75],"we":[76,139],"design":[77],"a":[78],"Low":[79],"Overhead":[80],"wireless":[81],"Traffic":[82],"Network":[84],"(LOTNet)":[85],"enhancement.":[88],"Firstly,":[89],"improve":[91],"under":[94],"city":[97],"Context":[100],"Embedding":[101],"Multi-Scale":[103],"Spatiotemporal":[104],"Expression":[105],"Long":[106],"Short":[107],"Term":[108],"Memory":[109],"(CMS-LSTM)":[110],"structure":[111,143],"dual":[114],"attention":[115],"mechanism":[116],"are":[117],"used":[118],"module":[122],"extraction":[125],"spatiotemporal":[127],"features.":[128],"Secondly,":[129],"reduce":[131],"overhead":[134],"caused":[135],"by":[136],"prediction,":[138],"use":[140],"autoencoder":[142],"hotspot":[145],"cells":[146],"extracting.":[147],"The":[148],"experiments":[149],"real":[151],"proves":[155],"effectiveness":[157],"our":[159],"scheme.":[160]},"counts_by_year":[],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}
