{"id":"https://openalex.org/W1592113329","doi":"https://doi.org/10.1109/pimrc.2014.7136354","title":"An efficient spectrum sensing algorithm for cognitive radio based on finite random matrix","display_name":"An efficient spectrum sensing algorithm for cognitive radio based on finite random matrix","publication_year":2014,"publication_date":"2014-09-01","ids":{"openalex":"https://openalex.org/W1592113329","doi":"https://doi.org/10.1109/pimrc.2014.7136354","mag":"1592113329"},"language":"en","primary_location":{"id":"doi:10.1109/pimrc.2014.7136354","is_oa":true,"landing_page_url":"https://doi.org/10.1109/pimrc.2014.7136354","pdf_url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7136354","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7136354","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011900098","display_name":"Fuhui Zhou","orcid":"https://orcid.org/0000-0001-6880-6244"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fuhui Zhou","raw_affiliation_strings":["State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, China","[State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019 an, China]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"[State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019 an, China]","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100697315","display_name":"Zan Li","orcid":"https://orcid.org/0000-0002-5207-6504"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zan Li","raw_affiliation_strings":["State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, China","[State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019 an, China]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"[State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019 an, China]","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009796023","display_name":"Jiangbo Si","orcid":"https://orcid.org/0000-0002-7419-4042"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangbo Si","raw_affiliation_strings":["[State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019 an, China]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"[State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019 an, China]","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042183747","display_name":"Lei Guan","orcid":"https://orcid.org/0000-0001-7191-887X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Guan","raw_affiliation_strings":["State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, China","[State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019 an, China]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"[State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019 an, China]","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":1.3799,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.78714375,"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":"1223","last_page":"1227"},"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":0.9997000098228455,"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":0.9997000098228455,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9952999949455261,"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/cholesky-decomposition","display_name":"Cholesky decomposition","score":0.9480180740356445},{"id":"https://openalex.org/keywords/minimum-degree-algorithm","display_name":"Minimum degree algorithm","score":0.7576191425323486},{"id":"https://openalex.org/keywords/eigendecomposition-of-a-matrix","display_name":"Eigendecomposition of a matrix","score":0.6724680662155151},{"id":"https://openalex.org/keywords/cognitive-radio","display_name":"Cognitive radio","score":0.6644907593727112},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6489672660827637},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.6222811341285706},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.5105939507484436},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.49316543340682983},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.4905659556388855},{"id":"https://openalex.org/keywords/false-alarm","display_name":"False alarm","score":0.4823262393474579},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.43908417224884033},{"id":"https://openalex.org/keywords/spectrum","display_name":"Spectrum (functional analysis)","score":0.4215030074119568},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.36828190088272095},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3638157546520233},{"id":"https://openalex.org/keywords/incomplete-cholesky-factorization","display_name":"Incomplete Cholesky factorization","score":0.17580682039260864},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11916345357894897},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.08390438556671143}],"concepts":[{"id":"https://openalex.org/C34727166","wikidata":"https://www.wikidata.org/wiki/Q515375","display_name":"Cholesky decomposition","level":3,"score":0.9480180740356445},{"id":"https://openalex.org/C46085209","wikidata":"https://www.wikidata.org/wiki/Q17098969","display_name":"Minimum degree algorithm","level":5,"score":0.7576191425323486},{"id":"https://openalex.org/C169756996","wikidata":"https://www.wikidata.org/wiki/Q194919","display_name":"Eigendecomposition of a matrix","level":3,"score":0.6724680662155151},{"id":"https://openalex.org/C149946192","wikidata":"https://www.wikidata.org/wiki/Q3235733","display_name":"Cognitive radio","level":3,"score":0.6644907593727112},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6489672660827637},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.6222811341285706},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.5105939507484436},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.49316543340682983},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.4905659556388855},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.4823262393474579},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.43908417224884033},{"id":"https://openalex.org/C156778621","wikidata":"https://www.wikidata.org/wiki/Q1365748","display_name":"Spectrum (functional analysis)","level":2,"score":0.4215030074119568},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.36828190088272095},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3638157546520233},{"id":"https://openalex.org/C44363057","wikidata":"https://www.wikidata.org/wiki/Q6015160","display_name":"Incomplete Cholesky factorization","level":4,"score":0.17580682039260864},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11916345357894897},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.08390438556671143},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/pimrc.2014.7136354","is_oa":true,"landing_page_url":"https://doi.org/10.1109/pimrc.2014.7136354","pdf_url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7136354","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1109/pimrc.2014.7136354","is_oa":true,"landing_page_url":"https://doi.org/10.1109/pimrc.2014.7136354","pdf_url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7136354","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.6299999952316284,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320327912","display_name":"Higher Education Discipline Innovation Project","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1974755392","https://openalex.org/W2028886213","https://openalex.org/W2041289314","https://openalex.org/W2045775723","https://openalex.org/W2055039038","https://openalex.org/W2065715415","https://openalex.org/W2071707134","https://openalex.org/W2088472442","https://openalex.org/W2101840010","https://openalex.org/W2118293214","https://openalex.org/W2129854524","https://openalex.org/W2136816673","https://openalex.org/W2150307184","https://openalex.org/W2163930010","https://openalex.org/W2219713589","https://openalex.org/W2461765758"],"related_works":["https://openalex.org/W2902935039","https://openalex.org/W2039814159","https://openalex.org/W3210095423","https://openalex.org/W2138563672","https://openalex.org/W1592113329","https://openalex.org/W2739538516","https://openalex.org/W2018841216","https://openalex.org/W2766670065","https://openalex.org/W2007907716","https://openalex.org/W2127054029"],"abstract_inverted_index":{"Spectrum":[0],"sensing":[1,32,59],"is":[2,47,61],"the":[3,12,24,44,56,74,78,81,94,110,120],"precondition":[4],"of":[5,7,43,65,80,106,109],"implementation":[6],"cognitive":[8],"radio.":[9],"Motivated":[10],"by":[11,49],"fact":[13],"that":[14,39,119],"eigenvalue":[15,21],"detection":[16],"algorithms":[17,129],"are":[18,84,101],"based":[19,34,130],"on":[20,35,73,131],"decomposition":[22,37,51],"over":[23,38,52],"covariance":[25,54,111],"matrix,":[26,55],"we":[27],"propose":[28],"an":[29],"efficient":[30,57],"spectrum":[31,58],"algorithm":[33,60,122],"Cholesky":[36,50],"matrix.":[40],"Using":[41],"eigenvalues":[42],"matrix":[45,83,112],"which":[46],"obtained":[48],"finite":[53,107],"proposed.":[62],"Attractive":[63],"advantages":[64],"our":[66],"proposed":[67],"technique":[68],"are:":[69],"a)":[70],"no":[71],"assumptions":[72],"sampling":[75],"size":[76,108],"and":[77,88,98,113],"dimension":[79],"random":[82],"required;":[85],"b)":[86],"exact":[87],"simple":[89],"closed-form":[90],"analytical":[91],"expressions":[92],"for":[93],"false":[95],"alarm":[96],"probability":[97],"decision":[99],"threshold":[100],"derived":[102],"under":[103],"practical":[104],"scenarios":[105],"samples;":[114],"c)":[115],"numerical":[116],"simulations":[117],"show":[118],"presented":[121],"achieves":[123],"performance":[124],"improvement":[125],"compared":[126],"with":[127],"previous":[128],"eigenvalue.":[132]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
