{"id":"https://openalex.org/W3106121695","doi":"https://doi.org/10.1109/iccc49849.2020.9238857","title":"Recovering Missing Values from Corrupted Historical Spectrum Observations for Dependable Spectrum Prediction","display_name":"Recovering Missing Values from Corrupted Historical Spectrum Observations for Dependable Spectrum Prediction","publication_year":2020,"publication_date":"2020-08-09","ids":{"openalex":"https://openalex.org/W3106121695","doi":"https://doi.org/10.1109/iccc49849.2020.9238857","mag":"3106121695"},"language":"en","primary_location":{"id":"doi:10.1109/iccc49849.2020.9238857","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccc49849.2020.9238857","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE/CIC International Conference on Communications in China (ICCC)","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/A5100407740","display_name":"Xi Li","orcid":"https://orcid.org/0000-0002-8914-5390"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]},{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]},{"id":"https://openalex.org/I1327163397","display_name":"State University of New York","ror":"https://ror.org/01q1z8k08","country_code":"US","type":"education","lineage":["https://openalex.org/I1327163397"]}],"countries":["CN","US"],"is_corresponding":true,"raw_author_name":"Xi Li","raw_affiliation_strings":["National Mobile Communication Research Lab, Southeast University, Nanjing, P. R. China","State University of New York at Stony Brook, USA"],"affiliations":[{"raw_affiliation_string":"National Mobile Communication Research Lab, Southeast University, Nanjing, P. R. China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"State University of New York at Stony Brook, USA","institution_ids":["https://openalex.org/I59553526","https://openalex.org/I1327163397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100745766","display_name":"Zhicheng Liu","orcid":"https://orcid.org/0000-0002-1015-2759"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhicheng Liu","raw_affiliation_strings":["National Mobile Communication Research Lab, Southeast University, Nanjing, P. R. China"],"affiliations":[{"raw_affiliation_string":"National Mobile Communication Research Lab, Southeast University, Nanjing, P. R. China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014227402","display_name":"Yinfei Xu","orcid":"https://orcid.org/0000-0003-3191-6903"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinfei Xu","raw_affiliation_strings":["National Mobile Communication Research Lab, Southeast University, Nanjing, P. R. China"],"affiliations":[{"raw_affiliation_string":"National Mobile Communication Research Lab, Southeast University, Nanjing, P. R. China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100327844","display_name":"Xin Wang","orcid":"https://orcid.org/0000-0001-8639-3818"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]},{"id":"https://openalex.org/I1327163397","display_name":"State University of New York","ror":"https://ror.org/01q1z8k08","country_code":"US","type":"education","lineage":["https://openalex.org/I1327163397"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xin Wang","raw_affiliation_strings":["State University of New York at Stony Brook, USA"],"affiliations":[{"raw_affiliation_string":"State University of New York at Stony Brook, USA","institution_ids":["https://openalex.org/I59553526","https://openalex.org/I1327163397"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022040924","display_name":"Tiecheng Song","orcid":"https://orcid.org/0000-0002-9505-0179"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tiecheng Song","raw_affiliation_strings":["National Mobile Communication Research Lab, Southeast University, Nanjing, P. R. China"],"affiliations":[{"raw_affiliation_string":"National Mobile Communication Research Lab, Southeast University, Nanjing, P. R. China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100407740"],"corresponding_institution_ids":["https://openalex.org/I1327163397","https://openalex.org/I59553526","https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":0.2822,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.4784217,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"941","last_page":"946"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9995999932289124,"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":0.9995999932289124,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9987000226974487,"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/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.9980999827384949,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/spectrum","display_name":"Spectrum (functional analysis)","score":0.7634269595146179},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.6145284175872803},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6037910580635071},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.5801132321357727},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.46697282791137695},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4648984968662262},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.44383251667022705},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.42706042528152466},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4095415472984314},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3587844967842102},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27758583426475525},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.20996835827827454}],"concepts":[{"id":"https://openalex.org/C156778621","wikidata":"https://www.wikidata.org/wiki/Q1365748","display_name":"Spectrum (functional analysis)","level":2,"score":0.7634269595146179},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.6145284175872803},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6037910580635071},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.5801132321357727},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.46697282791137695},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4648984968662262},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.44383251667022705},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.42706042528152466},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4095415472984314},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3587844967842102},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27758583426475525},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.20996835827827454},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccc49849.2020.9238857","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccc49849.2020.9238857","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE/CIC International Conference on Communications in China (ICCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1227527538","display_name":null,"funder_award_id":"30919013229","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G3759153026","display_name":null,"funder_award_id":"ECCS 1731238","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1995959580","https://openalex.org/W2013431023","https://openalex.org/W2018240663","https://openalex.org/W2033736958","https://openalex.org/W2128010550","https://openalex.org/W2135046866","https://openalex.org/W2142764821","https://openalex.org/W2144112805","https://openalex.org/W2153015273","https://openalex.org/W2169061815","https://openalex.org/W2209120354","https://openalex.org/W2469230926","https://openalex.org/W2512573859","https://openalex.org/W2602925055","https://openalex.org/W2607059137","https://openalex.org/W2755056899","https://openalex.org/W2785063405","https://openalex.org/W2790209388","https://openalex.org/W2885642955","https://openalex.org/W2889210442","https://openalex.org/W2895135437","https://openalex.org/W2908487597","https://openalex.org/W2909013530","https://openalex.org/W3105988367","https://openalex.org/W3106293402"],"related_works":["https://openalex.org/W2950186459","https://openalex.org/W2170114491","https://openalex.org/W2897298721","https://openalex.org/W2242624680","https://openalex.org/W2136127937","https://openalex.org/W4290987221","https://openalex.org/W2216309014","https://openalex.org/W2569661359","https://openalex.org/W3199841771","https://openalex.org/W4286971555"],"abstract_inverted_index":{"Spectrum":[0],"prediction":[1,39,79],"is":[2],"a":[3,89,116,138],"promising":[4],"technology":[5],"to":[6,24,55,62],"infer":[7],"spectrum":[8,12,36,38,73,78,92,106,112,141,167],"state":[9],"from":[10,70],"historical":[11,44,72,105],"observations,":[13,37],"by":[14,120],"exploiting":[15,121],"the":[16,25,34,57,67,104,111,133,147,155],"inherent":[17],"correlations":[18],"and":[19,31,42,64,100,126,161],"regularities":[20],"among":[21,103],"them.":[22],"Due":[23],"common":[26],"existence":[27],"of":[28],"missing":[29,68],"values":[30,69],"anomalies":[32],"in":[33],"real-world":[35],"with":[40,75],"incomplete":[41],"corrupted":[43,71],"observations":[45,74],"has":[46],"caused":[47],"extensive":[48],"concern.":[49],"In":[50],"this":[51,84],"paper,":[52],"we":[53,86,109,136],"aim":[54],"tackle":[56],"challenging":[58],"problem":[59,119],"on":[60,146],"how":[61],"accurately":[63],"efficiently":[65,131],"recover":[66],"which":[76],"dependable":[77,166],"can":[80,95],"be":[81],"performed.":[82],"To":[83,130],"end,":[85],"first":[87],"formulate":[88],"hankelized":[90],"time-structured":[91],"tensor":[93,117],"that":[94,154],"naturally":[96],"preserve":[97],"both":[98],"spectral":[99],"temporal":[101],"dependencies":[102],"observations.":[107],"Then":[108],"model":[110],"data":[113,142],"recovery":[114,143],"as":[115],"completion":[118],"its":[122,163],"latent":[123],"low-rank":[124],"structure":[125],"sparse":[127],"anomaly":[128],"property.":[129],"solve":[132],"optimization":[134],"problem,":[135],"design":[137],"robust":[139],"online":[140],"algorithm":[144,157],"based":[145],"alternating":[148],"direction":[149],"method.":[150],"Numerical":[151],"results":[152],"demonstrate":[153],"proposed":[156],"outperforms":[158],"state-of-the-art":[159],"schemes":[160],"confirm":[162],"effectiveness":[164],"for":[165],"prediction.":[168]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-14T08:04:32.555800","created_date":"2025-10-10T00:00:00"}