{"id":"https://openalex.org/W1979854409","doi":"https://doi.org/10.1109/glocom.2012.6503326","title":"Eigenvalue-based cyclostationary spectrum sensing using multiple antennas","display_name":"Eigenvalue-based cyclostationary spectrum sensing using multiple antennas","publication_year":2012,"publication_date":"2012-12-01","ids":{"openalex":"https://openalex.org/W1979854409","doi":"https://doi.org/10.1109/glocom.2012.6503326","mag":"1979854409"},"language":"en","primary_location":{"id":"doi:10.1109/glocom.2012.6503326","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocom.2012.6503326","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Global Communications Conference (GLOBECOM)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1210.8176","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Paulo Urriza","orcid":null},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Paulo Urriza","raw_affiliation_strings":["University of California Los Angeles, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California Los Angeles, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Eric Rebeiz","orcid":null},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eric Rebeiz","raw_affiliation_strings":["University of California Los Angeles, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California Los Angeles, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"last","author":{"id":null,"display_name":"Danijela Cabric","orcid":null},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Danijela Cabric","raw_affiliation_strings":["University of California Los Angeles, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California Los Angeles, USA","institution_ids":["https://openalex.org/I161318765"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I161318765"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1501","last_page":"1506"},"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":1.0,"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":1.0,"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/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"}},{"id":"https://openalex.org/T10891","display_name":"Radar Systems and Signal Processing","score":0.9883000254631042,"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/cyclostationary-process","display_name":"Cyclostationary process","score":0.8906000256538391},{"id":"https://openalex.org/keywords/cognitive-radio","display_name":"Cognitive radio","score":0.680899977684021},{"id":"https://openalex.org/keywords/false-alarm","display_name":"False alarm","score":0.629800021648407},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.5601000189781189},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5144000053405762},{"id":"https://openalex.org/keywords/spectrum","display_name":"Spectrum (functional analysis)","score":0.505299985408783},{"id":"https://openalex.org/keywords/rayleigh-fading","display_name":"Rayleigh fading","score":0.4074999988079071},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.3865000009536743},{"id":"https://openalex.org/keywords/constant-false-alarm-rate","display_name":"Constant false alarm rate","score":0.3840000033378601}],"concepts":[{"id":"https://openalex.org/C178351263","wikidata":"https://www.wikidata.org/wiki/Q3922399","display_name":"Cyclostationary process","level":3,"score":0.8906000256538391},{"id":"https://openalex.org/C149946192","wikidata":"https://www.wikidata.org/wiki/Q3235733","display_name":"Cognitive radio","level":3,"score":0.680899977684021},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6625000238418579},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.629800021648407},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5601000189781189},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5144000053405762},{"id":"https://openalex.org/C156778621","wikidata":"https://www.wikidata.org/wiki/Q1365748","display_name":"Spectrum (functional analysis)","level":2,"score":0.505299985408783},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4569999873638153},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4268999993801117},{"id":"https://openalex.org/C56985126","wikidata":"https://www.wikidata.org/wiki/Q854039","display_name":"Rayleigh fading","level":4,"score":0.4074999988079071},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.3865000009536743},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.3840000033378601},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.3801000118255615},{"id":"https://openalex.org/C169345407","wikidata":"https://www.wikidata.org/wiki/Q8216221","display_name":"Uncorrelated","level":2,"score":0.3797000050544739},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.37560001015663147},{"id":"https://openalex.org/C81978471","wikidata":"https://www.wikidata.org/wiki/Q1196572","display_name":"Fading","level":3,"score":0.34630000591278076},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.3278999924659729},{"id":"https://openalex.org/C2777027219","wikidata":"https://www.wikidata.org/wiki/Q1284190","display_name":"Constant (computer programming)","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C137270730","wikidata":"https://www.wikidata.org/wiki/Q120811","display_name":"Detection theory","level":3,"score":0.3021000027656555},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.29980000853538513},{"id":"https://openalex.org/C96608239","wikidata":"https://www.wikidata.org/wiki/Q1199823","display_name":"Statistical power","level":2,"score":0.2888999879360199},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.28299999237060547},{"id":"https://openalex.org/C163018871","wikidata":"https://www.wikidata.org/wiki/Q1302587","display_name":"Cross-correlation","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C187612029","wikidata":"https://www.wikidata.org/wiki/Q17083130","display_name":"Noise floor","level":4,"score":0.2768000066280365},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.25850000977516174},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/glocom.2012.6503326","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocom.2012.6503326","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Global Communications Conference (GLOBECOM)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1210.8176","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1210.8176","pdf_url":"https://arxiv.org/pdf/1210.8176","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1210.8176","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1210.8176","pdf_url":"https://arxiv.org/pdf/1210.8176","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1967892878","https://openalex.org/W2034063243","https://openalex.org/W2071707134","https://openalex.org/W2094502128","https://openalex.org/W2101840010","https://openalex.org/W2109187222","https://openalex.org/W2114074734","https://openalex.org/W2124590431","https://openalex.org/W2136816673","https://openalex.org/W2144991509","https://openalex.org/W2164233625","https://openalex.org/W2171402290","https://openalex.org/W2172139273","https://openalex.org/W6676801445"],"related_works":[],"abstract_inverted_index":{"In":[0,47],"this":[1,78],"paper,":[2],"we":[3],"propose":[4],"a":[5,58,128],"signal-selective":[6],"spectrum":[7,124],"sensing":[8,125],"method":[9,23,80,112],"for":[10,17,26,71],"cognitive":[11],"radio":[12],"networks":[13],"and":[14,86,98,137],"specifically":[15],"targeted":[16],"receivers":[18],"with":[19],"multiple-antenna":[20,122],"capability.":[21],"This":[22],"is":[24,54,81,113],"used":[25,55,97],"detecting":[27],"the":[28,37,40,49,93,99,104],"presence":[29],"or":[30],"absence":[31],"of":[32,39,44,64,91,95],"primary":[33],"users":[34],"based":[35],"on":[36,106],"eigenvalues":[38],"cyclic":[41,50,66],"covariance":[42],"matrix":[43],"received":[45],"signals.":[46],"particular,":[48],"correlation":[51],"significance":[52],"test":[53],"to":[56,88,119],"detect":[57],"specific":[59],"signal-of-interest":[60],"by":[61],"exploiting":[62],"knowledge":[63],"its":[65],"frequencies.":[67],"The":[68,110,141],"analytical":[69],"threshold":[70],"achieving":[72],"constant":[73],"false":[74],"alarm":[75],"rate":[76],"using":[77],"detection":[79],"presented,":[82],"verified":[83],"through":[84,116],"simulations,":[85,118],"shown":[87],"be":[89],"independent":[90],"both":[92,134],"number":[94],"samples":[96],"noise":[100,108,139],"variance,":[101],"effectively":[102],"eliminating":[103],"dependence":[105],"accurate":[107],"estimation.":[109],"proposed":[111],"also":[114,143],"shown,":[115],"numerical":[117],"outperform":[120],"existing":[121],"cyclostationary-based":[123],"algorithms":[126],"under":[127],"quasi-static":[129],"Rayleigh":[130],"fading":[131],"channel,":[132],"in":[133],"spatially":[135],"correlated":[136],"uncorrelated":[138],"environments.":[140],"algorithm":[142],"has":[144],"significantly":[145],"lower":[146],"computational":[147],"complexity":[148],"than":[149],"these":[150],"other":[151],"approaches.":[152]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2013,"cited_by_count":2}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2016-06-24T00:00:00"}
