{"id":"https://openalex.org/W2620988915","doi":"https://doi.org/10.1109/tccn.2017.2710309","title":"Cooperatively Learning Footprints of Multiple Incumbent Transmitters by Using Cognitive Radio Networks","display_name":"Cooperatively Learning Footprints of Multiple Incumbent Transmitters by Using Cognitive Radio Networks","publication_year":2017,"publication_date":"2017-05-31","ids":{"openalex":"https://openalex.org/W2620988915","doi":"https://doi.org/10.1109/tccn.2017.2710309","mag":"2620988915"},"language":"en","primary_location":{"id":"doi:10.1109/tccn.2017.2710309","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tccn.2017.2710309","pdf_url":null,"source":{"id":"https://openalex.org/S2484188435","display_name":"IEEE Transactions on Cognitive Communications and Networking","issn_l":"2332-7731","issn":["2332-7731","2372-2045"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Cognitive Communications and Networking","raw_type":"journal-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/A5054758780","display_name":"Mihir Laghate","orcid":"https://orcid.org/0000-0001-9852-9719"},"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":true,"raw_author_name":"Mihir Laghate","raw_affiliation_strings":["Department of Electrical Engineering, University of California at Los Angeles, Los Angeles, CA, USA"],"raw_orcid":"https://orcid.org/0000-0001-9852-9719","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of California at Los Angeles, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008128583","display_name":"Danijela \u010cabri\u0107","orcid":"https://orcid.org/0000-0002-5967-2683"},"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":["Department of Electrical Engineering, University of California at Los Angeles, Los Angeles, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of California at Los Angeles, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5054758780"],"corresponding_institution_ids":["https://openalex.org/I161318765"],"apc_list":null,"apc_paid":null,"fwci":3.2894,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.93088839,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"3","issue":"3","first_page":"282","last_page":"297"},"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":0.9998999834060669,"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":0.9998999834060669,"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/T11158","display_name":"Wireless Networks and Protocols","score":0.9976999759674072,"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":0.9948999881744385,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8452921509742737},{"id":"https://openalex.org/keywords/cognitive-radio","display_name":"Cognitive radio","score":0.7518695592880249},{"id":"https://openalex.org/keywords/aloha","display_name":"Aloha","score":0.7291379570960999},{"id":"https://openalex.org/keywords/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.5043569803237915},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4631350338459015},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.4350162744522095},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.36647793650627136},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33593010902404785},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3355313539505005},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32733750343322754},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.25668296217918396},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.11920428276062012}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8452921509742737},{"id":"https://openalex.org/C149946192","wikidata":"https://www.wikidata.org/wiki/Q3235733","display_name":"Cognitive radio","level":3,"score":0.7518695592880249},{"id":"https://openalex.org/C2776398200","wikidata":"https://www.wikidata.org/wiki/Q508880","display_name":"Aloha","level":4,"score":0.7291379570960999},{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.5043569803237915},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4631350338459015},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.4350162744522095},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.36647793650627136},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33593010902404785},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3355313539505005},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32733750343322754},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.25668296217918396},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.11920428276062012}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tccn.2017.2710309","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tccn.2017.2710309","pdf_url":null,"source":{"id":"https://openalex.org/S2484188435","display_name":"IEEE Transactions on Cognitive Communications and Networking","issn_l":"2332-7731","issn":["2332-7731","2372-2045"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Cognitive Communications and Networking","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8899999856948853}],"awards":[{"id":"https://openalex.org/G3745163964","display_name":null,"funder_award_id":"1527026","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1523338928","https://openalex.org/W1981300371","https://openalex.org/W2007739089","https://openalex.org/W2011039300","https://openalex.org/W2028449080","https://openalex.org/W2031211320","https://openalex.org/W2042920982","https://openalex.org/W2049468127","https://openalex.org/W2049633694","https://openalex.org/W2049844934","https://openalex.org/W2054508838","https://openalex.org/W2070291375","https://openalex.org/W2072267094","https://openalex.org/W2079234446","https://openalex.org/W2080090262","https://openalex.org/W2103542269","https://openalex.org/W2103749883","https://openalex.org/W2127383773","https://openalex.org/W2133858407","https://openalex.org/W2134556395","https://openalex.org/W2134698708","https://openalex.org/W2140136927","https://openalex.org/W2140318696","https://openalex.org/W2140452629","https://openalex.org/W2149436559","https://openalex.org/W2149655761","https://openalex.org/W2155999145","https://openalex.org/W2157916023","https://openalex.org/W2164098787","https://openalex.org/W2165202277","https://openalex.org/W2292885983","https://openalex.org/W2337621958","https://openalex.org/W2343899768","https://openalex.org/W2476096155","https://openalex.org/W2962790067","https://openalex.org/W3156192964","https://openalex.org/W4233274640","https://openalex.org/W6680770193","https://openalex.org/W6681793532"],"related_works":["https://openalex.org/W2020802031","https://openalex.org/W2093360341","https://openalex.org/W2018726158","https://openalex.org/W2387652801","https://openalex.org/W3023940508","https://openalex.org/W2540573036","https://openalex.org/W1990934859","https://openalex.org/W2053336077","https://openalex.org/W2335557509","https://openalex.org/W2158337294"],"abstract_inverted_index":{"Energy":[0],"measurements":[1],"have":[2],"conventionally":[3],"been":[4],"used":[5,150],"for":[6,15,44,61,131],"detecting":[7],"the":[8,42,48,68,82,133,153,157,171,176],"presence":[9],"of":[10,81,84,144,159,182],"signal":[11],"energy":[12],"but":[13],"not":[14,72],"distinguishing":[16,62],"incumbent":[17],"users":[18],"(IUs).":[19],"In":[20,167],"this":[21],"paper,":[22],"soft":[23,90,172],"and":[24,39,124,162],"hard":[25,111],"reports":[26,91,112,173],"Gaussian":[27],"mixture":[28],"model":[29],"learning":[30,118],"algorithms":[31],"are":[32,149,165],"proposed":[33,69,189],"to":[34,40,96,107,151,190,198],"distinguish":[35],"intermittently":[36],"transmitting":[37],"IUs":[38,63,85],"find":[41],"footprints":[43],"each":[45,56,122],"IU,":[46],"i.e.,":[47],"cognitive":[49],"radio":[50],"(CRs)":[51],"that":[52,187],"receive":[53],"signals":[54],"from":[55],"IU.":[57],"Unlike":[58],"existing":[59],"methods":[60,70,188],"using":[64,127],"single":[65],"antenna":[66],"CRs,":[67,160],"do":[71],"require":[73],"CR":[74,123],"locations,":[75],"channel":[76,199],"models,":[77],"or":[78,86],"prior":[79],"knowledge":[80],"number":[83,158],"their":[87],"protocol.":[88],"The":[89,110],"algorithm":[92,113,130,174],"uses":[93],"Mahalanobis":[94],"distance":[95],"separate":[97],"components":[98,105],"while":[99],"a":[100,128,139],"two":[101],"stage":[102],"process":[103],"learns":[104],"corresponding":[106],"individual":[108],"IUs.":[109],"reduces":[114],"computational":[115],"complexity":[116],"by":[117],"unidimensional":[119],"mixtures":[120],"at":[121],"fusing":[125],"results":[126],"novel":[129],"finding":[132],"maximum":[134],"weight":[135],"dominating":[136],"set":[137],"in":[138],"directed":[140],"graph.":[141],"MATLAB":[142],"simulations":[143,181],"slotted":[145],"ALOHA":[146],"IU":[147,184],"networks":[148,185],"evaluate":[152],"algorithms'":[154],"performance":[155,196],"as":[156],"IUs,":[161],"average":[163],"activity":[164],"varied.":[166],"frequent":[168],"collision":[169],"scenarios,":[170],"has":[175],"best":[177],"performance.":[178],"However,":[179],"NS3":[180],"802.11n":[183],"show":[186],"reduce":[191],"false":[192],"positives":[193],"deteriorate":[194],"detection":[195],"due":[197],"capture":[200],"effects.":[201]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}