{"id":"https://openalex.org/W2783222692","doi":"https://doi.org/10.1109/glocom.2017.8254114","title":"Spectrum Sensing and Power Classification in Spatially Correlated Noise Scenarios","display_name":"Spectrum Sensing and Power Classification in Spatially Correlated Noise Scenarios","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2783222692","doi":"https://doi.org/10.1109/glocom.2017.8254114","mag":"2783222692"},"language":"en","primary_location":{"id":"doi:10.1109/glocom.2017.8254114","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocom.2017.8254114","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2017 - 2017 IEEE Global Communications Conference","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100632499","display_name":"Danyang Wang","orcid":"https://orcid.org/0000-0001-6930-5628"},"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":"Danyang Wang","raw_affiliation_strings":["State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Networks, Xidian University, Xi'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 Service Networks, Xidian University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100404886","display_name":"Ning Zhang","orcid":"https://orcid.org/0000-0002-8781-4925"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Ning Zhang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100773343","display_name":"Xuemin Shen","orcid":"https://orcid.org/0000-0002-4140-287X"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Xuemin Shen","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2248,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.44551867,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"55","issue":null,"first_page":"1","last_page":"6"},"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/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.9951000213623047,"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"}},{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9932000041007996,"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/underlay","display_name":"Underlay","score":0.8742417097091675},{"id":"https://openalex.org/keywords/cognitive-radio","display_name":"Cognitive radio","score":0.7838031053543091},{"id":"https://openalex.org/keywords/transmission","display_name":"Transmission (telecommunications)","score":0.7487457394599915},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7191802263259888},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6348955631256104},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6301860809326172},{"id":"https://openalex.org/keywords/noise-power","display_name":"Noise power","score":0.6280688643455505},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.4910585880279541},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.4831528067588806},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.4594714045524597},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.4516701400279999},{"id":"https://openalex.org/keywords/signal-to-noise-ratio","display_name":"Signal-to-noise ratio (imaging)","score":0.39564576745033264},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.31158560514450073},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.30195310711860657},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.1706947386264801},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1409102976322174},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1346326470375061},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08567309379577637},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07379019260406494}],"concepts":[{"id":"https://openalex.org/C2777679929","wikidata":"https://www.wikidata.org/wiki/Q7883709","display_name":"Underlay","level":3,"score":0.8742417097091675},{"id":"https://openalex.org/C149946192","wikidata":"https://www.wikidata.org/wiki/Q3235733","display_name":"Cognitive radio","level":3,"score":0.7838031053543091},{"id":"https://openalex.org/C761482","wikidata":"https://www.wikidata.org/wiki/Q118093","display_name":"Transmission (telecommunications)","level":2,"score":0.7487457394599915},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7191802263259888},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6348955631256104},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6301860809326172},{"id":"https://openalex.org/C203234222","wikidata":"https://www.wikidata.org/wiki/Q2133519","display_name":"Noise power","level":3,"score":0.6280688643455505},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.4910585880279541},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.4831528067588806},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4594714045524597},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.4516701400279999},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.39564576745033264},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.31158560514450073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30195310711860657},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.1706947386264801},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1409102976322174},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1346326470375061},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08567309379577637},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07379019260406494},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/glocom.2017.8254114","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocom.2017.8254114","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2017 - 2017 IEEE Global Communications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2013069968","https://openalex.org/W2038279219","https://openalex.org/W2067181210","https://openalex.org/W2071707134","https://openalex.org/W2096127572","https://openalex.org/W2099762801","https://openalex.org/W2100909350","https://openalex.org/W2113749736","https://openalex.org/W2122274174","https://openalex.org/W2127593435","https://openalex.org/W2139182243","https://openalex.org/W2155999145","https://openalex.org/W2343330270","https://openalex.org/W2509733445","https://openalex.org/W2964255670"],"related_works":["https://openalex.org/W2769590643","https://openalex.org/W2169642774","https://openalex.org/W2532161418","https://openalex.org/W2556201531","https://openalex.org/W2290175079","https://openalex.org/W2365415733","https://openalex.org/W1644717624","https://openalex.org/W2063322879","https://openalex.org/W4200379864","https://openalex.org/W2130267323"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,76,91],"spectrum":[6,84],"sensing":[7,112],"and":[8,29],"power":[9,62,79,143],"classification":[10],"scheme":[11,89,136],"in":[12],"hybrid":[13],"interweave-":[14],"underlay":[15,73],"cognitive":[16],"radio":[17],"networks,":[18],"considering":[19],"that":[20,67],"the":[21,30,33,46,49,54,60,64,68,83,96,100,105,110,116,120,130,140,146,150,159],"primary":[22,41,50],"system":[23],"is":[24,37,43,57,90],"with":[25,75],"multiple":[26],"transmission":[27,61,78,132,142],"powers":[28],"noise":[31,151],"at":[32],"secondary":[34,55],"user":[35,51],"(SU)":[36],"spatially":[38],"correlated.":[39],"The":[40,87,134],"target":[42,56],"to":[44,58,72,157],"detect":[45],"presence":[47],"of":[48,63,99,119,149],"(PU),":[52],"while":[53],"classify":[59],"PU,":[65],"such":[66],"SU":[69],"can":[70,137],"switch":[71],"model":[74],"flexible":[77],"for":[80,114,128],"fully":[81],"exploring":[82],"access":[85],"opportunities.":[86],"proposed":[88,135,160],"non-coherent":[92],"detection":[93],"scheme,":[94],"where":[95],"weighted":[97],"energy":[98],"received":[101],"signals":[102],"serves":[103],"as":[104,122,124],"decision":[106,126],"metric.":[107],"We":[108],"derive":[109],"optimal":[111],"threshold":[113],"detecting":[115],"``on/off\"":[117],"state":[118],"PU":[121],"well":[123],"closed-form":[125],"thresholds":[127],"classifying":[129],"PU's":[131,141],"power.":[133],"efficiently":[138],"identify":[139],"by":[144],"leveraging":[145],"correlation":[147],"information":[148],"observations.":[152],"Simulation":[153],"results":[154],"are":[155],"provided":[156],"evaluate":[158],"scheme.":[161]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
