{"id":"https://openalex.org/W2796142626","doi":"https://doi.org/10.1109/iccchina.2017.8330356","title":"Oracle approximating shrinkage estimator based cooperative spectrum sensing for dense cognitive small cell network","display_name":"Oracle approximating shrinkage estimator based cooperative spectrum sensing for dense cognitive small cell network","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2796142626","doi":"https://doi.org/10.1109/iccchina.2017.8330356","mag":"2796142626"},"language":"en","primary_location":{"id":"doi:10.1109/iccchina.2017.8330356","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccchina.2017.8330356","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 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/A5067002746","display_name":"Meng Zhao","orcid":"https://orcid.org/0009-0004-2051-7936"},"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":"Meng Zhao","raw_affiliation_strings":["Beijing Laboratory of Advanced Information Networks, Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing Laboratory of Advanced Information Networks, Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058648594","display_name":"Caili Guo","orcid":"https://orcid.org/0000-0001-8892-4520"},"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":"Caili Guo","raw_affiliation_strings":["Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050046664","display_name":"Chunyan Feng","orcid":"https://orcid.org/0000-0002-4277-6857"},"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":"Chunyan Feng","raw_affiliation_strings":["Beijing Laboratory of Advanced Information Networks, Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing Laboratory of Advanced Information Networks, Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100366851","display_name":"Shuo Chen","orcid":"https://orcid.org/0000-0001-7453-8282"},"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":"Shuo Chen","raw_affiliation_strings":["Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5067002746"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.27339163,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"4","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10579","display_name":"Cognitive Radio Networks and Spectrum Sensing","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10579","display_name":"Cognitive Radio Networks and Spectrum Sensing","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.9976000189781189,"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"}},{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/estimator","display_name":"Estimator","score":0.690027117729187},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.6513312458992004},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5906929969787598},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5643889904022217},{"id":"https://openalex.org/keywords/oracle","display_name":"Oracle","score":0.5225117206573486},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4892749488353729},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.48888295888900757},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.4853459894657135},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.46273407340049744},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.42900368571281433},{"id":"https://openalex.org/keywords/shrinkage-estimator","display_name":"Shrinkage estimator","score":0.42679211497306824},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.42130744457244873},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.413917601108551},{"id":"https://openalex.org/keywords/bias-of-an-estimator","display_name":"Bias of an estimator","score":0.36624059081077576},{"id":"https://openalex.org/keywords/minimum-variance-unbiased-estimator","display_name":"Minimum-variance unbiased estimator","score":0.2619650959968567},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17695575952529907},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07747188210487366}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.690027117729187},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.6513312458992004},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5906929969787598},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5643889904022217},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.5225117206573486},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4892749488353729},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.48888295888900757},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4853459894657135},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46273407340049744},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.42900368571281433},{"id":"https://openalex.org/C102592046","wikidata":"https://www.wikidata.org/wiki/Q7504144","display_name":"Shrinkage estimator","level":5,"score":0.42679211497306824},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.42130744457244873},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.413917601108551},{"id":"https://openalex.org/C191393472","wikidata":"https://www.wikidata.org/wiki/Q15222032","display_name":"Bias of an estimator","level":4,"score":0.36624059081077576},{"id":"https://openalex.org/C165646398","wikidata":"https://www.wikidata.org/wiki/Q3755281","display_name":"Minimum-variance unbiased estimator","level":3,"score":0.2619650959968567},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17695575952529907},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07747188210487366},{"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/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccchina.2017.8330356","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccchina.2017.8330356","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE/CIC International Conference on Communications in China (ICCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"No poverty","id":"https://metadata.un.org/sdg/1","score":0.4300000071525574}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1492953540","https://openalex.org/W1990677539","https://openalex.org/W2017987111","https://openalex.org/W2056916045","https://openalex.org/W2062125287","https://openalex.org/W2090796575","https://openalex.org/W2105502962","https://openalex.org/W2119963163","https://openalex.org/W2147153302","https://openalex.org/W2150397642","https://openalex.org/W2249359021","https://openalex.org/W2335288635","https://openalex.org/W2508936987","https://openalex.org/W2589046494","https://openalex.org/W2590209931","https://openalex.org/W2724433343","https://openalex.org/W3103394902","https://openalex.org/W6740098004"],"related_works":["https://openalex.org/W3123301109","https://openalex.org/W2999701797","https://openalex.org/W2017635138","https://openalex.org/W2988435853","https://openalex.org/W2554611575","https://openalex.org/W2984458635","https://openalex.org/W1940209753","https://openalex.org/W1669801202","https://openalex.org/W2770583480","https://openalex.org/W3122432023"],"abstract_inverted_index":{"In":[0,21,129],"this":[1],"paper,":[2],"we":[3,79,118],"study":[4],"the":[5,24,30,40,120,130],"problem":[6],"that":[7,143],"dense":[8,25],"cognitive":[9],"small":[10,28,33],"cells":[11,34],"cooperate":[12],"to":[13,39,69,165],"sense":[14],"primary":[15],"signals":[16],"of":[17,23,27,32,42,55,64,71,114,123,125],"a":[18],"macro":[19],"cell.":[20],"consideration":[22],"deployment":[26],"cells,":[29],"number":[31,41],"(sample":[35,44],"dimension)":[36],"is":[37,52,100,110,135],"comparable":[38],"sample":[43,49,65,105,176,179],"size),":[45],"in":[46,112],"which":[47,99],"case":[48,113],"covariance":[50,58,66,106],"matrix":[51,67],"ill-conditioned":[53],"estimator":[54,85,98],"high-dimensional":[56,156],"statistical":[57],"matrix.":[59,107],"The":[60],"poor":[61],"estimated":[62],"performance":[63,150,161],"leads":[68],"degradation":[70],"sensing":[72,89,160],"performance.":[73],"Therefore,":[74],"based":[75,86],"on":[76],"Neyman-Pearson":[77],"theorem,":[78],"propose":[80],"two":[81],"oracle":[82,94],"approximating":[83,95],"shrinkage":[84,96],"cooperative":[87],"spectrum":[88],"(OAS-CSS)":[90],"algorithms":[91],"by":[92],"utilizing":[93],"(OAS)":[97],"more":[101],"accurate":[102],"compared":[103],"with":[104,163,172],"First":[108],"method":[109],"proposed":[111,145],"ideal":[115,166],"noise":[116,134,167],"and":[117,127,154,178],"derive":[119],"theoretical":[121,159],"expressions":[122],"probability":[124],"false":[126],"threshold.":[128],"latter":[131],"method,":[132],"non-ideal":[133],"considered":[136],"whose":[137],"variances":[138],"are":[139,181],"imbalanced.":[140],"Simulations":[141],"show":[142,168],"our":[144],"OAS-CSS":[146],"detectors":[147,153],"exhibit":[148],"better":[149],"than":[151],"traditional":[152],"existing":[155],"detectors.":[157],"Also,":[158],"results":[162,174],"respect":[164],"an":[169],"excellent":[170],"agreement":[171],"simulation":[173],"when":[175],"dimension":[177],"size":[180],"large.":[182]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
