{"id":"https://openalex.org/W2601738911","doi":"https://doi.org/10.1109/vtcfall.2016.7881083","title":"Mean Spectral Radius Detection for Cognitive Radio","display_name":"Mean Spectral Radius Detection for Cognitive Radio","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2601738911","doi":"https://doi.org/10.1109/vtcfall.2016.7881083","mag":"2601738911"},"language":"en","primary_location":{"id":"doi:10.1109/vtcfall.2016.7881083","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtcfall.2016.7881083","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 84th Vehicular Technology Conference (VTC-Fall)","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/A5101484838","display_name":"Yulong Gao","orcid":"https://orcid.org/0000-0003-2433-4075"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yulong Gao","raw_affiliation_strings":["Communication Research Center, Harbin Institute of Technology, Harbin, China"],"affiliations":[{"raw_affiliation_string":"Communication Research Center, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005570500","display_name":"Xinsheng Han","orcid":null},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinsheng Han","raw_affiliation_strings":["Communication Research Center, Harbin Institute of Technology, Harbin, China"],"affiliations":[{"raw_affiliation_string":"Communication Research Center, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027119125","display_name":"Yongkui Ma","orcid":"https://orcid.org/0000-0002-9535-5167"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongkui Ma","raw_affiliation_strings":["Communication Research Center, Harbin Institute of Technology, Harbin, China"],"affiliations":[{"raw_affiliation_string":"Communication Research Center, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101484838"],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.22887935,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"40","issue":null,"first_page":"1","last_page":"5"},"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":0.9998000264167786,"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":0.9998000264167786,"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":0.9973999857902527,"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/T10891","display_name":"Radar Systems and Signal Processing","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/hermitian-matrix","display_name":"Hermitian matrix","score":0.7941714525222778},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.7016206383705139},{"id":"https://openalex.org/keywords/cognitive-radio","display_name":"Cognitive radio","score":0.6752484440803528},{"id":"https://openalex.org/keywords/random-matrix","display_name":"Random matrix","score":0.6559457778930664},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.5874605774879456},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5573832392692566},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.556505024433136},{"id":"https://openalex.org/keywords/spectrum","display_name":"Spectrum (functional analysis)","score":0.5108959078788757},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.49598440527915955},{"id":"https://openalex.org/keywords/spectral-radius","display_name":"Spectral radius","score":0.48225337266921997},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.47797223925590515},{"id":"https://openalex.org/keywords/signal-to-noise-ratio","display_name":"Signal-to-noise ratio (imaging)","score":0.4708397388458252},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4569757878780365},{"id":"https://openalex.org/keywords/radius","display_name":"RADIUS","score":0.4415942430496216},{"id":"https://openalex.org/keywords/eigendecomposition-of-a-matrix","display_name":"Eigendecomposition of a matrix","score":0.42704272270202637},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.42196768522262573},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.343380868434906},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3067833185195923},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.2429124414920807},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.23028168082237244},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.19452178478240967},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.15429523587226868},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.10112720727920532},{"id":"https://openalex.org/keywords/pure-mathematics","display_name":"Pure mathematics","score":0.08379125595092773}],"concepts":[{"id":"https://openalex.org/C94940","wikidata":"https://www.wikidata.org/wiki/Q652941","display_name":"Hermitian matrix","level":2,"score":0.7941714525222778},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.7016206383705139},{"id":"https://openalex.org/C149946192","wikidata":"https://www.wikidata.org/wiki/Q3235733","display_name":"Cognitive radio","level":3,"score":0.6752484440803528},{"id":"https://openalex.org/C64812099","wikidata":"https://www.wikidata.org/wiki/Q176604","display_name":"Random matrix","level":3,"score":0.6559457778930664},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5874605774879456},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5573832392692566},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.556505024433136},{"id":"https://openalex.org/C156778621","wikidata":"https://www.wikidata.org/wiki/Q1365748","display_name":"Spectrum (functional analysis)","level":2,"score":0.5108959078788757},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.49598440527915955},{"id":"https://openalex.org/C140532419","wikidata":"https://www.wikidata.org/wiki/Q249748","display_name":"Spectral radius","level":3,"score":0.48225337266921997},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.47797223925590515},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.4708397388458252},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4569757878780365},{"id":"https://openalex.org/C178635117","wikidata":"https://www.wikidata.org/wiki/Q747499","display_name":"RADIUS","level":2,"score":0.4415942430496216},{"id":"https://openalex.org/C169756996","wikidata":"https://www.wikidata.org/wiki/Q194919","display_name":"Eigendecomposition of a matrix","level":3,"score":0.42704272270202637},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.42196768522262573},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.343380868434906},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3067833185195923},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.2429124414920807},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23028168082237244},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.19452178478240967},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.15429523587226868},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.10112720727920532},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.08379125595092773},{"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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtcfall.2016.7881083","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtcfall.2016.7881083","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 84th Vehicular Technology Conference (VTC-Fall)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1535757228","https://openalex.org/W1761295751","https://openalex.org/W1932472321","https://openalex.org/W1964474545","https://openalex.org/W2000798729","https://openalex.org/W2041289314","https://openalex.org/W2090796575","https://openalex.org/W2107300868","https://openalex.org/W2110766716","https://openalex.org/W2136816673","https://openalex.org/W2170524206","https://openalex.org/W2267872997","https://openalex.org/W2275208256","https://openalex.org/W2378028109","https://openalex.org/W2544603002","https://openalex.org/W7046380255"],"related_works":["https://openalex.org/W4309155225","https://openalex.org/W2072321776","https://openalex.org/W3099782216","https://openalex.org/W2016499627","https://openalex.org/W1968062933","https://openalex.org/W1668678746","https://openalex.org/W3104367283","https://openalex.org/W4385810767","https://openalex.org/W2952374288","https://openalex.org/W4366732362"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"a":[3,33],"new":[4],"spectrum":[5],"sensing":[6,20,117],"algorithm":[7],"is":[8,73],"proposed":[9,94,131],"based":[10],"on":[11],"the":[12,16,27,43,47,58,63,66,70,78,91,97,106,113,127,130],"eigenvalue":[13,44,71,109],"distribution":[14,45,72],"of":[15,19,25,49,69,77,129],"covariance":[17],"matrix":[18],"nodes.":[21],"The":[22,93],"received":[23],"signals":[24,115],"all":[26],"nodes":[28,118],"can":[29,86],"be":[30,87],"denoted":[31],"by":[32],"non-":[34],"Hermitian":[35],"random":[36,51],"matrix.":[37],"A":[38],"recent":[39],"research":[40],"indicates":[41],"that":[42,76],"for":[46,57,62],"product":[48],"non-Hermitian":[50],"matrices":[52],"follows":[53],"Single":[54],"Ring":[55],"Theorem":[56],"noise-only":[59,79],"case.":[60,80],"However,":[61],"signal-present":[64],"case,":[65],"inner":[67],"radius":[68,84],"smaller":[74],"than":[75,105],"Then":[81],"mean":[82],"spectral":[83],"(MSR)":[85],"utilized":[88],"to":[89,125],"detect":[90],"signal.":[92],"method":[95],"overcomes":[96],"noise":[98],"uncertainty":[99],"and":[100],"has":[101],"higher":[102],"detection":[103,111],"performance":[104],"maximum-":[107],"minimum":[108],"(MME)":[110],"when":[112],"primary":[114],"among":[116],"are":[119,123],"uncorrelated.":[120],"Finally,":[121],"Simulations":[122],"performed":[124],"verify":[126],"effectiveness":[128],"method.":[132]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
