{"id":"https://openalex.org/W2516178161","doi":"https://doi.org/10.1109/icufn.2016.7537024","title":"Exact quickest spectrum sensing algorithms for eigenvalue-based change detection","display_name":"Exact quickest spectrum sensing algorithms for eigenvalue-based change detection","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2516178161","doi":"https://doi.org/10.1109/icufn.2016.7537024","mag":"2516178161"},"language":"en","primary_location":{"id":"doi:10.1109/icufn.2016.7537024","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icufn.2016.7537024","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN)","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/A5022882474","display_name":"Martijn Arts","orcid":null},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Martijn Arts","raw_affiliation_strings":["Institute for Theoretical Information Technology, RWTH Aachen University, Aachen, Germany"],"affiliations":[{"raw_affiliation_string":"Institute for Theoretical Information Technology, RWTH Aachen University, Aachen, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006615819","display_name":"Andreas Bollig","orcid":null},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andreas Bollig","raw_affiliation_strings":["Institute for Theoretical Information Technology, RWTH Aachen University, Aachen, Germany"],"affiliations":[{"raw_affiliation_string":"Institute for Theoretical Information Technology, RWTH Aachen University, Aachen, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066277118","display_name":"Rudolf Mathar","orcid":"https://orcid.org/0000-0002-9585-605X"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Rudolf Mathar","raw_affiliation_strings":["Institute for Theoretical Information Technology, RWTH Aachen University, Aachen, Germany"],"affiliations":[{"raw_affiliation_string":"Institute for Theoretical Information Technology, RWTH Aachen University, Aachen, Germany","institution_ids":["https://openalex.org/I887968799"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5022882474"],"corresponding_institution_ids":["https://openalex.org/I887968799"],"apc_list":null,"apc_paid":null,"fwci":0.8898,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.78625361,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"235","last_page":"240"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10579","display_name":"Cognitive Radio Networks and Spectrum Sensing","score":0.9878000020980835,"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/wishart-distribution","display_name":"Wishart distribution","score":0.8080160617828369},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.7099217772483826},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.5971587896347046},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.5758726596832275},{"id":"https://openalex.org/keywords/test-statistic","display_name":"Test statistic","score":0.5564819574356079},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.5298882126808167},{"id":"https://openalex.org/keywords/phase-shift-keying","display_name":"Phase-shift keying","score":0.5296071767807007},{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.5165942907333374},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4765634834766388},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4539480209350586},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.4112951457500458},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.39864030480384827},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.31596267223358154},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2700662612915039},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.12124347686767578},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.09343293309211731},{"id":"https://openalex.org/keywords/bit-error-rate","display_name":"Bit error rate","score":0.07699599862098694}],"concepts":[{"id":"https://openalex.org/C33962027","wikidata":"https://www.wikidata.org/wiki/Q1930697","display_name":"Wishart distribution","level":3,"score":0.8080160617828369},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.7099217772483826},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.5971587896347046},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.5758726596832275},{"id":"https://openalex.org/C169857963","wikidata":"https://www.wikidata.org/wiki/Q1461038","display_name":"Test statistic","level":3,"score":0.5564819574356079},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5298882126808167},{"id":"https://openalex.org/C186378180","wikidata":"https://www.wikidata.org/wiki/Q4874866","display_name":"Phase-shift keying","level":4,"score":0.5296071767807007},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.5165942907333374},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4765634834766388},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4539480209350586},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.4112951457500458},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.39864030480384827},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.31596267223358154},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2700662612915039},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.12124347686767578},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.09343293309211731},{"id":"https://openalex.org/C56296756","wikidata":"https://www.wikidata.org/wiki/Q840922","display_name":"Bit error rate","level":3,"score":0.07699599862098694},{"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/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"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":2,"locations":[{"id":"doi:10.1109/icufn.2016.7537024","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icufn.2016.7537024","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN)","raw_type":"proceedings-article"},{"id":"pmh:oai:publications.rwth-aachen.de:661196","is_oa":false,"landing_page_url":"https://publications.rwth-aachen.de/record/661196","pdf_url":null,"source":{"id":"https://openalex.org/S4306401362","display_name":"RWTH Publications (RWTH Aachen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887968799","host_organization_name":"RWTH Aachen University","host_organization_lineage":["https://openalex.org/I887968799"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ICUFN 2016 : July 5 (Tue.)-July 8 (Fri.), 2016, Technische Universit\u00e4t (TU) Wien, Vienna, Austria : the Eighth International Conference on Ubiquitous and Future Networks / IEEE, IEEE Communications Society, KICS - The Korean Institute of Communications and Information Sciences, Springer, IEICE Communications Society<br/>8. International Conference on Ubiquitous and Future Networks, ICUFN 2016, Vienna, Austria, 2016-07-05 - 2016-07-08","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1974637548","https://openalex.org/W1981904416","https://openalex.org/W2000798729","https://openalex.org/W2028317156","https://openalex.org/W2038514455","https://openalex.org/W2044046421","https://openalex.org/W2044535354","https://openalex.org/W2051903196","https://openalex.org/W2055039038","https://openalex.org/W2092431085","https://openalex.org/W2105563843","https://openalex.org/W2107300868","https://openalex.org/W2131570846","https://openalex.org/W2142373847","https://openalex.org/W2149898670","https://openalex.org/W2153620960","https://openalex.org/W2163930010","https://openalex.org/W2236737411","https://openalex.org/W2544603002","https://openalex.org/W4249116379","https://openalex.org/W6675777979"],"related_works":["https://openalex.org/W2545896082","https://openalex.org/W2122033380","https://openalex.org/W2162726981","https://openalex.org/W2115358448","https://openalex.org/W2388824665","https://openalex.org/W3111630311","https://openalex.org/W2157094190","https://openalex.org/W1965215867","https://openalex.org/W2164444766","https://openalex.org/W2100598284"],"abstract_inverted_index":{"We":[0,109],"study":[1],"a":[2,9,20,25,66],"collaborative":[3],"quickest":[4,101,146],"detection":[5,59,102,116,147,153],"scheme":[6],"that":[7],"uses":[8],"function":[10,80],"of":[11,14,32,82,114],"the":[12,15,46,61,77,83,111,122,144,150],"eigenvalues":[13],"sample":[16,62],"covariance":[17,63],"matrix":[18,64],"for":[19,57,104],"spectrum":[21,119],"sensing":[22],"system":[23],"with":[24,149],"fusion":[26],"center.":[27],"A":[28],"simple":[29],"model":[30],"consisting":[31],"one":[33],"potentially":[34],"present":[35],"primary":[36],"user":[37],"(PU),":[38],"which":[39],"utilizes":[40],"phase":[41],"shift":[42],"keying":[43],"(PSK),":[44],"and":[45,106,131,141],"standard":[47],"additive":[48],"white":[49],"Gaussian":[50],"noise":[51],"(AWGN)":[52],"assumption":[53],"is":[54,136],"considered.":[55],"Here,":[56],"both":[58,94],"hypothesis,":[60],"follows":[65],"Wishart":[67],"distribution.":[68],"For":[69],"K":[70],"=":[71],"2":[72],"collaborating":[73],"secondary":[74],"users":[75],"(SUs),":[76],"probability":[78],"distribution":[79],"(PDF)":[81],"maximum-minimum":[84],"eigenvalue":[85],"(MME)":[86],"test":[87],"statistic":[88],"can":[89],"be":[90],"derived":[91],"analytically":[92],"under":[93],"hypotheses,":[95],"allowing":[96],"us":[97],"to":[98],"develop":[99],"exact":[100],"algorithms":[103,148],"known":[105],"unknown":[107],"SNR.":[108],"analyze":[110],"two":[112],"types":[113],"change":[115],"problems":[117],"in":[118],"sensing,":[120],"i.e.,":[121],"channel":[123],"becoming":[124],"free":[125],"when":[126],"it":[127],"was":[128],"occupied":[129],"before":[130],"vice":[132],"versa.":[133],"Performance":[134],"evaluation":[135],"done":[137],"by":[138,142],"evaluating":[139],"bounds":[140],"comparing":[143],"presented":[145],"traditional":[151],"block":[152],"scheme.":[154]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}