{"id":"https://openalex.org/W4414404629","doi":"https://doi.org/10.1109/lwc.2025.3612947","title":"Area-Based Eigenvalue Spectrum Sensing for CRNs Using Marchenko\u2013Pastur Bounds","display_name":"Area-Based Eigenvalue Spectrum Sensing for CRNs Using Marchenko\u2013Pastur Bounds","publication_year":2025,"publication_date":"2025-09-22","ids":{"openalex":"https://openalex.org/W4414404629","doi":"https://doi.org/10.1109/lwc.2025.3612947"},"language":"en","primary_location":{"id":"doi:10.1109/lwc.2025.3612947","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lwc.2025.3612947","pdf_url":null,"source":{"id":"https://openalex.org/S2500830676","display_name":"IEEE Wireless Communications Letters","issn_l":"2162-2337","issn":["2162-2337","2162-2345"],"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 Wireless Communications Letters","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/A5119694985","display_name":"Sithumini Sankalpana","orcid":null},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Sithumini Sankalpana","raw_affiliation_strings":["School of Engineering, Macquarie University, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0009-0000-0556-8744","affiliations":[{"raw_affiliation_string":"School of Engineering, Macquarie University, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I99043593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056452912","display_name":"Ediz Cetin","orcid":"https://orcid.org/0000-0002-9313-3034"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Ediz Cetin","raw_affiliation_strings":["School of Engineering, Macquarie University, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0002-9313-3034","affiliations":[{"raw_affiliation_string":"School of Engineering, Macquarie University, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I99043593"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110824873","display_name":"Sam Reisenfeld","orcid":null},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Sam Reisenfeld","raw_affiliation_strings":["School of Engineering, Macquarie University, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0002-8254-7055","affiliations":[{"raw_affiliation_string":"School of Engineering, Macquarie University, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I99043593"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8948,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.78305528,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"14","issue":"12","first_page":"4067","last_page":"4071"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10046","display_name":"Stability and Control of Uncertain Systems","score":0.9280999898910522,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10046","display_name":"Stability and Control of Uncertain Systems","score":0.9280999898910522,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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":0.9254000186920166,"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/cognitive-radio","display_name":"Cognitive radio","score":0.6966999769210815},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6554999947547913},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.6039000153541565},{"id":"https://openalex.org/keywords/random-matrix","display_name":"Random matrix","score":0.5827999711036682},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.57669997215271},{"id":"https://openalex.org/keywords/wishart-distribution","display_name":"Wishart distribution","score":0.4738999903202057},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.4259999990463257},{"id":"https://openalex.org/keywords/test-statistic","display_name":"Test statistic","score":0.4253999888896942},{"id":"https://openalex.org/keywords/false-alarm","display_name":"False alarm","score":0.41690000891685486},{"id":"https://openalex.org/keywords/detection-theory","display_name":"Detection theory","score":0.4011000096797943}],"concepts":[{"id":"https://openalex.org/C149946192","wikidata":"https://www.wikidata.org/wiki/Q3235733","display_name":"Cognitive radio","level":3,"score":0.6966999769210815},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6554999947547913},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.6039000153541565},{"id":"https://openalex.org/C64812099","wikidata":"https://www.wikidata.org/wiki/Q176604","display_name":"Random matrix","level":3,"score":0.5827999711036682},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.57669997215271},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5694000124931335},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5231999754905701},{"id":"https://openalex.org/C33962027","wikidata":"https://www.wikidata.org/wiki/Q1930697","display_name":"Wishart distribution","level":3,"score":0.4738999903202057},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.4259999990463257},{"id":"https://openalex.org/C169857963","wikidata":"https://www.wikidata.org/wiki/Q1461038","display_name":"Test statistic","level":3,"score":0.4253999888896942},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.41690000891685486},{"id":"https://openalex.org/C137270730","wikidata":"https://www.wikidata.org/wiki/Q120811","display_name":"Detection theory","level":3,"score":0.4011000096797943},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.4009000062942505},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.38920000195503235},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.38839998841285706},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.38100001215934753},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37070000171661377},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.3650999963283539},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.35249999165534973},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.351500004529953},{"id":"https://openalex.org/C156778621","wikidata":"https://www.wikidata.org/wiki/Q1365748","display_name":"Spectrum (functional analysis)","level":2,"score":0.35100001096725464},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.3310999870300293},{"id":"https://openalex.org/C169756996","wikidata":"https://www.wikidata.org/wiki/Q194919","display_name":"Eigendecomposition of a matrix","level":3,"score":0.3091000020503998},{"id":"https://openalex.org/C103784038","wikidata":"https://www.wikidata.org/wiki/Q386228","display_name":"Cumulative distribution function","level":3,"score":0.3075000047683716},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C168110828","wikidata":"https://www.wikidata.org/wiki/Q1331626","display_name":"Spectral density","level":2,"score":0.2953999936580658},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.290800005197525},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.28040000796318054},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C116544410","wikidata":"https://www.wikidata.org/wiki/Q1478122","display_name":"Shadow mapping","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C96608239","wikidata":"https://www.wikidata.org/wiki/Q1199823","display_name":"Statistical power","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.2513999938964844},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.2506999969482422},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lwc.2025.3612947","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lwc.2025.3612947","pdf_url":null,"source":{"id":"https://openalex.org/S2500830676","display_name":"IEEE Wireless Communications Letters","issn_l":"2162-2337","issn":["2162-2337","2162-2345"],"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 Wireless Communications Letters","raw_type":"journal-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/W615589970","https://openalex.org/W753656334","https://openalex.org/W1579971684","https://openalex.org/W1965748679","https://openalex.org/W2060581589","https://openalex.org/W2110704086","https://openalex.org/W2156860647","https://openalex.org/W2249359021","https://openalex.org/W2507441606","https://openalex.org/W2589046494","https://openalex.org/W2603244479","https://openalex.org/W2669298917","https://openalex.org/W2899726877","https://openalex.org/W2907930484","https://openalex.org/W4229746864"],"related_works":[],"abstract_inverted_index":{"A":[0],"novel":[1],"eigenvalue-based":[2],"spectrum":[3],"sensing":[4],"technique":[5],"for":[6],"cognitive":[7],"radio":[8],"networks":[9],"is":[10],"presented.":[11],"The":[12],"method":[13],"leverages":[14],"the":[15,46,50,56,61],"behavior":[16],"of":[17,65],"a":[18,33,74],"Wishart":[19],"matrix":[20],"characterized":[21],"by":[22],"Marchenko-Pastur":[23],"bounds":[24],"based":[25],"on":[26,43],"Random":[27],"Matrix":[28],"Theory":[29],"(RMT)":[30],"to":[31,95,102],"construct":[32,73],"robust":[34],"detection":[35,80,109],"framework.":[36],"Unlike":[37],"conventional":[38],"methods":[39],"that":[40,77],"rely":[41],"solely":[42],"extreme":[44],"eigenvalues,":[45],"proposed":[47],"approach":[48],"utilizes":[49],"entire":[51],"empirical":[52],"eigenvalue":[53],"distribution":[54],"within":[55],"Marchenko\u2013Pastur":[57],"bounds.":[58],"By":[59],"integrating":[60],"probability":[62],"density":[63,70],"function":[64],"eigenvalues":[66],"estimated":[67],"via":[68],"kernel":[69],"estimation,":[71],"we":[72],"test":[75],"statistic":[76],"significantly":[78],"enhances":[79],"performance,":[81],"particularly":[82],"under":[83],"low":[84],"Signal-to-Noise":[85],"Ratio":[86],"(SNR)":[87],"conditions.":[88],"Simulation":[89],"results":[90],"demonstrate":[91],"superior":[92],"performance":[93],"compared":[94],"traditional":[96],"energy":[97],"detection,":[98],"with":[99],"improved":[100],"robustness":[101],"noise":[103],"uncertainty":[104],"and":[105,111],"flexibility":[106],"in":[107],"balancing":[108],"accuracy":[110],"computational":[112],"efficiency.":[113]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-13T07:54:00.901334","created_date":"2025-10-10T00:00:00"}
