{"id":"https://openalex.org/W3192474343","doi":"https://doi.org/10.1109/icc42927.2021.9500681","title":"Unsupervised Two-Stage Learning Framework for Cooperative Spectrum Sensing","display_name":"Unsupervised Two-Stage Learning Framework for Cooperative Spectrum Sensing","publication_year":2021,"publication_date":"2021-06-01","ids":{"openalex":"https://openalex.org/W3192474343","doi":"https://doi.org/10.1109/icc42927.2021.9500681","mag":"3192474343"},"language":"en","primary_location":{"id":"doi:10.1109/icc42927.2021.9500681","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc42927.2021.9500681","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2021 - IEEE International Conference on Communications","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/A5055511593","display_name":"Nada Abdel Khalek","orcid":"https://orcid.org/0000-0001-9024-5367"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Nada Abdel Khalek","raw_affiliation_strings":["Concordia University,Department of Electrical and Computer Engineering,Montreal,Canada,H3G 1M8"],"affiliations":[{"raw_affiliation_string":"Concordia University,Department of Electrical and Computer Engineering,Montreal,Canada,H3G 1M8","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005243649","display_name":"Walaa Hamouda","orcid":"https://orcid.org/0000-0001-6618-5851"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Walaa Hamouda","raw_affiliation_strings":["Concordia University,Department of Electrical and Computer Engineering,Montreal,Canada,H3G 1M8"],"affiliations":[{"raw_affiliation_string":"Concordia University,Department of Electrical and Computer Engineering,Montreal,Canada,H3G 1M8","institution_ids":["https://openalex.org/I60158472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5055511593"],"corresponding_institution_ids":["https://openalex.org/I60158472"],"apc_list":null,"apc_paid":null,"fwci":1.9201,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.8652789,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"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":0.9988999962806702,"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":0.9988999962806702,"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":0.9959999918937683,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9890000224113464,"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/computer-science","display_name":"Computer science","score":0.7130756378173828},{"id":"https://openalex.org/keywords/stage","display_name":"Stage (stratigraphy)","score":0.6649914979934692},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4977853298187256},{"id":"https://openalex.org/keywords/spectrum","display_name":"Spectrum (functional analysis)","score":0.49462077021598816},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4482715427875519},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37178927659988403},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.10598552227020264},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07121342420578003}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7130756378173828},{"id":"https://openalex.org/C146357865","wikidata":"https://www.wikidata.org/wiki/Q1123245","display_name":"Stage (stratigraphy)","level":2,"score":0.6649914979934692},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4977853298187256},{"id":"https://openalex.org/C156778621","wikidata":"https://www.wikidata.org/wiki/Q1365748","display_name":"Spectrum (functional analysis)","level":2,"score":0.49462077021598816},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4482715427875519},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37178927659988403},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.10598552227020264},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07121342420578003},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icc42927.2021.9500681","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc42927.2021.9500681","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2021 - IEEE International Conference on Communications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1488270586","https://openalex.org/W1510073064","https://openalex.org/W2049633694","https://openalex.org/W2065424480","https://openalex.org/W2083400779","https://openalex.org/W2091005538","https://openalex.org/W2119821739","https://openalex.org/W2551956255","https://openalex.org/W2553535241","https://openalex.org/W2774614268","https://openalex.org/W2775184687","https://openalex.org/W2810631324","https://openalex.org/W2889040741","https://openalex.org/W2982286025","https://openalex.org/W3011894868","https://openalex.org/W3094255352","https://openalex.org/W3125995405","https://openalex.org/W4239510810"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"A":[0,140],"cognitive":[1],"radio":[2,32],"(CR)":[3],"network":[4,206,210],"consists":[5],"of":[6,30,97,123,134,176,203],"wireless":[7],"devices":[8],"that":[9,40,215],"opportunistically":[10],"borrow":[11],"vacant":[12],"licensed":[13],"bands.":[14],"Cognitive":[15],"users":[16],"adaptively":[17],"employ":[18],"a":[19,59,86,170,221],"perception-action":[20],"decision":[21],"cycle.":[22],"Learning-based":[23],"CR":[24,38,63,82,104,205],"networks":[25,83],"use":[26,41],"past":[27],"acquired":[28],"knowledge":[29],"the":[31,70,91,95,120,124,135,146,153,161,177,185,189,195,200,204,216,226,231,237],"environment":[33],"to":[34,236],"make":[35],"smarter":[36],"decisions.":[37],"systems":[39],"supervised":[42],"learning":[43,111,218],"for":[44,50,62,113,184],"spectrum":[45,115],"sensing":[46],"require":[47],"labeled":[48,56,98],"data":[49,57,99,133],"training":[51,132],"purposes.":[52],"Having":[53],"readily":[54],"available":[55],"is":[58,77,143,149,182],"complex":[60],"task":[61],"networks,":[64],"as":[65],"it":[66],"requires":[67],"cooperation":[68,76,87],"between":[69],"primary":[71,209],"and":[72,84,129,145,158,169,188,229],"secondary":[73],"users.":[74],"Such":[75],"not":[78],"possible":[79],"in":[80,101,174],"interweave":[81],"imposes":[85],"overhead.":[88],"Motivated":[89],"by":[90],"above,":[92],"we":[93,198],"tackle":[94],"problem":[96],"scarcity":[100],"practical":[102],"learning-based":[103],"networks.":[105],"We":[106,165],"propose":[107],"an":[108,167],"unsupervised":[109],"two-stage":[110,191,217],"framework":[112],"cooperative":[114],"sensing.":[116],"The":[117,179],"system":[118,141,192],"combines":[119],"superior":[121],"performance":[122,148,172,181,202,224,235],"Support":[125],"Vector":[126],"Machine":[127],"(SVM)":[128],"low":[130],"cost":[131],"Gaussian":[136],"Mixture":[137],"Model":[138],"(GMM).":[139],"model":[142],"proposed,":[144],"system\u2019s":[147],"evaluated":[150],"based":[151,193],"on":[152,194],"Receiver":[154],"Operating":[155],"Characteristics":[156],"(ROC)":[157],"Area":[159],"Under":[160],"ROC":[162],"Curve":[163],"(AUC).":[164],"obtain":[166],"upper":[168],"lower":[171],"bounds":[173],"terms":[175],"AUC.":[178],"detection":[180,201,223],"compared":[183],"SVM,":[186],"GMM,":[187],"proposed":[190],"ROC.":[196],"Additionally,":[197],"evaluate":[199],"under":[207],"different":[208],"sizes.":[211],"Our":[212],"results":[213],"show":[214],"approach":[219],"attains":[220],"higher":[222],"than":[225],"GMM":[227],"algorithm,":[228],"achieves":[230],"same":[232],"or":[233],"comparable":[234],"SVM":[238],"algorithm.":[239]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}