{"id":"https://openalex.org/W4400064506","doi":"https://doi.org/10.1109/gcwkshps68340.2025.11590940","title":"Modelling the 5G Energy Consumption Using Real-world Data: Energy Fingerprint is All You Need","display_name":"Modelling the 5G Energy Consumption Using Real-world Data: Energy Fingerprint is All You Need","publication_year":2025,"publication_date":"2025-12-08","ids":{"openalex":"https://openalex.org/W4400064506","doi":"https://doi.org/10.1109/gcwkshps68340.2025.11590940"},"language":"en","primary_location":{"id":"doi:10.1109/gcwkshps68340.2025.11590940","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcwkshps68340.2025.11590940","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Globecom Workshops (GC Wkshps)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2406.16929","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101975372","display_name":"Ting-Wei Chen","orcid":"https://orcid.org/0009-0003-0590-4789"},"institutions":[{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingwei Chen","raw_affiliation_strings":["Shenzhen Research Institute of Big Data,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen Research Institute of Big Data,Shenzhen,China","institution_ids":["https://openalex.org/I4210099586"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101818528","display_name":"Yantao Wang","orcid":"https://orcid.org/0000-0003-4968-5867"},"institutions":[{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yantao Wang","raw_affiliation_strings":["Shenzhen Research Institute of Big Data,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen Research Institute of Big Data,Shenzhen,China","institution_ids":["https://openalex.org/I4210099586"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hanzhi Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Hanzhi Chen","raw_affiliation_strings":["Nanyang Technological University,School of Electrical and Electronic Engineering,Singapore,Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanyang Technological University,School of Electrical and Electronic Engineering,Singapore,Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078420686","display_name":"Zijian Zhao","orcid":"https://orcid.org/0000-0002-7849-814X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zijian Zhao","raw_affiliation_strings":["Sun Yat-sen University,School of Computer Science and Engineering,Guangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University,School of Computer Science and Engineering,Guangzhou,China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064771919","display_name":"Xin\u2010Hao Li","orcid":"https://orcid.org/0000-0003-1643-4631"},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinhao Li","raw_affiliation_strings":["The Chinese University of Hong Kong (Shenzhen),School of Science and Engineering,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong (Shenzhen),School of Science and Engineering,Shenzhen,China","institution_ids":["https://openalex.org/I4210116924"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048382270","display_name":"Nicola Piovesan","orcid":"https://orcid.org/0000-0001-5397-5248"},"institutions":[{"id":"https://openalex.org/I4210123571","display_name":"Huawei Technologies (France)","ror":"https://ror.org/02rbzf697","country_code":"FR","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210123571"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Nicola Piovesan","raw_affiliation_strings":["Huawei Technologies,Boulogne-Billancourt,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Technologies,Boulogne-Billancourt,France","institution_ids":["https://openalex.org/I4210123571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101884776","display_name":"Guangxu Zhu","orcid":"https://orcid.org/0000-0003-3116-2726"},"institutions":[{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangxu Zhu","raw_affiliation_strings":["Shenzhen Research Institute of Big Data,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen Research Institute of Big Data,Shenzhen,China","institution_ids":["https://openalex.org/I4210099586"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059252324","display_name":"Qingjiang Shi","orcid":"https://orcid.org/0000-0003-0507-9080"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingjiang Shi","raw_affiliation_strings":["Tongji University,School of Software Engineering,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tongji University,School of Software Engineering,Shanghai,China","institution_ids":["https://openalex.org/I116953780"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00071797,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1675","last_page":"1680"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10768","display_name":"Electric Vehicles and Infrastructure","score":0.9178000092506409,"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/T10768","display_name":"Electric Vehicles and Infrastructure","score":0.9178000092506409,"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/fingerprint","display_name":"Fingerprint (computing)","score":0.7119052410125732},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.60042405128479},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.5833929777145386},{"id":"https://openalex.org/keywords/consumption","display_name":"Consumption (sociology)","score":0.524651050567627},{"id":"https://openalex.org/keywords/real-world-data","display_name":"Real world data","score":0.4968438446521759},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4402100443840027},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.28802746534347534},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.25848978757858276},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16721493005752563},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1604623794555664},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12657266855239868},{"id":"https://openalex.org/keywords/art","display_name":"Art","score":0.1250019371509552},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.0705302357673645}],"concepts":[{"id":"https://openalex.org/C2777826928","wikidata":"https://www.wikidata.org/wiki/Q3745713","display_name":"Fingerprint (computing)","level":2,"score":0.7119052410125732},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.60042405128479},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.5833929777145386},{"id":"https://openalex.org/C30772137","wikidata":"https://www.wikidata.org/wiki/Q5164762","display_name":"Consumption (sociology)","level":2,"score":0.524651050567627},{"id":"https://openalex.org/C3020493868","wikidata":"https://www.wikidata.org/wiki/Q55631277","display_name":"Real world data","level":2,"score":0.4968438446521759},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4402100443840027},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28802746534347534},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.25848978757858276},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16721493005752563},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1604623794555664},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12657266855239868},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.1250019371509552},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0705302357673645},{"id":"https://openalex.org/C107038049","wikidata":"https://www.wikidata.org/wiki/Q35986","display_name":"Aesthetics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/gcwkshps68340.2025.11590940","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcwkshps68340.2025.11590940","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Globecom Workshops (GC Wkshps)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2406.16929","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2406.16929","pdf_url":"https://arxiv.org/pdf/2406.16929","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"doi:10.48550/arxiv.2406.16929","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2406.16929","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2406.16929","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2406.16929","pdf_url":"https://arxiv.org/pdf/2406.16929","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4400064506.pdf","grobid_xml":"https://content.openalex.org/works/W4400064506.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2339806289","https://openalex.org/W4297745244","https://openalex.org/W4214628662","https://openalex.org/W2156822401","https://openalex.org/W2037518538","https://openalex.org/W3092380670","https://openalex.org/W3125847301","https://openalex.org/W4313390786","https://openalex.org/W2089393798","https://openalex.org/W2356819012"],"abstract_inverted_index":{"The":[0,144],"introduction":[1],"of":[2],"5G":[3,66],"technology":[4],"has":[5,19],"revolutionized":[6],"communications,":[7],"enabling":[8,45],"unprecedented":[9],"capacity,":[10],"connectivity,":[11],"and":[12,111,118],"ultra-fast,":[13],"reliable":[14],"communications.":[15],"However,":[16],"this":[17],"leap":[18],"led":[20],"to":[21,47,115,133,141],"a":[22,29,60,74,107],"substantial":[23],"increase":[24],"in":[25],"energy":[26,36,69,98],"consumption,":[27],"presenting":[28],"critical":[30],"challenge":[31],"for":[32,41,65,147],"network":[33,53],"sustainability.":[34],"Accurate":[35],"consumption":[37,70],"modeling":[38],"is":[39,150],"essential":[40],"developing":[42],"energy-efficient":[43],"strategies,":[44],"operators":[46],"optimize":[48],"resource":[49],"utilization":[50],"while":[51],"maintaining":[52],"performance.":[54],"To":[55],"address":[56],"this,":[57],"we":[58],"propose":[59],"novel":[61],"deep":[62],"learning":[63],"model":[64,149],"base":[67,102],"station":[68],"estimation":[71],"based":[72],"on":[73],"real-world":[75],"dataset.":[76],"Unlike":[77],"existing":[78,142],"methods,":[79],"our":[80,148],"approach":[81],"integrates":[82],"the":[83],"Base":[84],"Station":[85],"Identifier":[86],"(BSID)":[87],"as":[88],"an":[89,93,112],"input":[90],"feature":[91],"through":[92],"embedding":[94],"layer,":[95],"capturing":[96],"unique":[97],"patterns":[99],"across":[100],"different":[101],"stations.":[103],"We":[104],"further":[105],"introduce":[106],"masked":[108],"training":[109],"method":[110],"attention":[113],"mechanism":[114],"enhance":[116],"generalization":[117],"accuracy.":[119],"Experimental":[120],"results":[121],"show":[122],"significant":[123],"improvements,":[124],"reducing":[125],"Mean":[126],"Absolute":[127],"Percentage":[128],"Error":[129],"(MAPE)":[130],"from":[131],"12.75%":[132],"4.98%,":[134],"achieving":[135],"over":[136],"60%":[137],"performance":[138],"gain":[139],"compared":[140],"models.":[143],"source":[145],"code":[146],"available":[151],"at":[152],"https://github.com/RS2002/ARL.":[153]},"counts_by_year":[],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2024-06-27T00:00:00"}