{"id":"https://openalex.org/W4389544913","doi":"https://doi.org/10.1109/vtc2023-fall60731.2023.10333857","title":"Adaptive Weighted Tensor Completion: A Solution to Joint Denoising and Periodic Prediction of Spectrum","display_name":"Adaptive Weighted Tensor Completion: A Solution to Joint Denoising and Periodic Prediction of Spectrum","publication_year":2023,"publication_date":"2023-10-10","ids":{"openalex":"https://openalex.org/W4389544913","doi":"https://doi.org/10.1109/vtc2023-fall60731.2023.10333857"},"language":"en","primary_location":{"id":"doi:10.1109/vtc2023-fall60731.2023.10333857","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/vtc2023-fall60731.2023.10333857","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall)","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/A5104211667","display_name":"Wanyu An","orcid":null},"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":"Wanyu An","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Wireless Signal Processing and Networks Laboratory,Beijing,China","Wireless Signal Processing and Networks Laboratory, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Wireless Signal Processing and Networks Laboratory,Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Wireless Signal Processing and Networks Laboratory, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041007168","display_name":"Zhuo Sun","orcid":"https://orcid.org/0000-0002-3333-722X"},"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":"Zhuo Sun","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Wireless Signal Processing and Networks Laboratory,Beijing,China","Wireless Signal Processing and Networks Laboratory, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Wireless Signal Processing and Networks Laboratory,Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Wireless Signal Processing and Networks Laboratory, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083177788","display_name":"Gang Yue","orcid":"https://orcid.org/0000-0001-5646-0944"},"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":"Gang Yue","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Wireless Signal Processing and Networks Laboratory,Beijing,China","Wireless Signal Processing and Networks Laboratory, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Wireless Signal Processing and Networks Laboratory,Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Wireless Signal Processing and Networks Laboratory, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"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":0.23300971,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"8","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.9836999773979187,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9750000238418579,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6146908402442932},{"id":"https://openalex.org/keywords/matrix-norm","display_name":"Matrix norm","score":0.6031796932220459},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5932109355926514},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5445733666419983},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.50393146276474},{"id":"https://openalex.org/keywords/spectral-density","display_name":"Spectral density","score":0.4926641583442688},{"id":"https://openalex.org/keywords/relaxation","display_name":"Relaxation (psychology)","score":0.48877760767936707},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4462394714355469},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.41556182503700256},{"id":"https://openalex.org/keywords/interference","display_name":"Interference (communication)","score":0.4109492599964142},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39475521445274353},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3135322332382202},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.21100068092346191},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09807413816452026}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6146908402442932},{"id":"https://openalex.org/C92207270","wikidata":"https://www.wikidata.org/wiki/Q939253","display_name":"Matrix norm","level":3,"score":0.6031796932220459},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5932109355926514},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5445733666419983},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.50393146276474},{"id":"https://openalex.org/C168110828","wikidata":"https://www.wikidata.org/wiki/Q1331626","display_name":"Spectral density","level":2,"score":0.4926641583442688},{"id":"https://openalex.org/C2776029896","wikidata":"https://www.wikidata.org/wiki/Q3935810","display_name":"Relaxation (psychology)","level":2,"score":0.48877760767936707},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4462394714355469},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.41556182503700256},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.4109492599964142},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39475521445274353},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3135322332382202},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21100068092346191},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09807413816452026},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtc2023-fall60731.2023.10333857","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/vtc2023-fall60731.2023.10333857","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.6800000071525574}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2024165284","https://openalex.org/W2043571470","https://openalex.org/W2091449379","https://openalex.org/W2128010550","https://openalex.org/W2146278756","https://openalex.org/W2528907418","https://openalex.org/W2552259797","https://openalex.org/W2889210442","https://openalex.org/W2909013530","https://openalex.org/W2964214749","https://openalex.org/W2988379071"],"related_works":["https://openalex.org/W2375786911","https://openalex.org/W2941091020","https://openalex.org/W2367630196","https://openalex.org/W2134887131","https://openalex.org/W756944427","https://openalex.org/W2349547031","https://openalex.org/W2953204310","https://openalex.org/W2030927653","https://openalex.org/W2062063412","https://openalex.org/W3155724094"],"abstract_inverted_index":{"The":[0],"recent":[1],"proposed":[2,200],"long-term":[3],"spectrum":[4,29,36,62,90,137,180,193],"prediction":[5,50,58,177],"is":[6,145],"a":[7,12,25,71,96,140,153,162,167],"promising":[8],"technology":[9],"to":[10,131,147,174],"predict":[11],"power":[13],"spectral":[14,72],"image":[15,73],"from":[16,40,92],"time-frequency":[17],"dimensions":[18],"in":[19,28],"the":[20,56,60,68,81,86,102,105,110,114,118,133,136,158,171,196,199],"next":[21],"period,":[22],"which":[23],"plays":[24],"crucial":[26],"role":[27],"management":[30],"and":[31,43,77,113,181,191],"electronic":[32],"countermeasures.":[33],"However,":[34],"real-world":[35,192],"data":[37,41,91,194],"often":[38],"suffer":[39],"loss":[42],"noise":[44,111,142,151],"interference,":[45],"posing":[46],"challenges":[47],"of":[48,59,70,88,104,135,178,183,198],"ensuring":[49],"accuracy.":[51],"This":[52],"paper":[53],"focuses":[54],"on":[55,64,157,188],"periodic":[57],"future":[61,179],"based":[63],"corrupted":[65,184],"measurements,":[66],"enabling":[67],"generation":[69],"that":[74,165],"captures":[75],"frequencies":[76],"time":[78],"slots":[79],"for":[80],"subsequent":[82],"period.":[83],"We":[84],"formulate":[85],"task":[87],"recovering":[89],"noisy":[93],"observations":[94],"as":[95],"tensor":[97,107],"completion":[98,172],"problem":[99],"by":[100],"minimizing":[101],"sum":[103],"weighted":[106],"nuclear":[108],"norm,":[109],"variance,":[112],"deviation":[115],"values":[116],"at":[117],"missing":[119],"position.":[120],"For":[121],"this":[122],"optimization":[123],"problem,":[124],"we":[125,160],"employ":[126],"an":[127],"adaptive":[128],"non-convex":[129],"relaxation":[130],"approximate":[132],"rank":[134],"tensor.":[138],"Additionally,":[139],"generic":[141],"Frobenius":[143],"term":[144],"adopted":[146],"effectively":[148],"eliminate":[149],"arbitrary":[150],"with":[152],"certain":[154],"distribution.":[155],"Based":[156],"model,":[159],"propose":[161],"joint":[163],"method":[164],"integrates":[166],"pre-fill":[168],"operation":[169],"into":[170],"model":[173],"enable":[175],"simultaneous":[176],"recovery":[182],"historical":[185],"data.":[186],"Experiments":[187],"both":[189],"synthetic":[190],"verify":[195],"effectiveness":[197],"method.":[201]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
