{"id":"https://openalex.org/W1992316120","doi":"https://doi.org/10.1109/icc.2012.6364244","title":"Cyclostationary-based low complexity wideband spectrum sensing using compressive sampling","display_name":"Cyclostationary-based low complexity wideband spectrum sensing using compressive sampling","publication_year":2012,"publication_date":"2012-06-01","ids":{"openalex":"https://openalex.org/W1992316120","doi":"https://doi.org/10.1109/icc.2012.6364244","mag":"1992316120"},"language":"en","primary_location":{"id":"doi:10.1109/icc.2012.6364244","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc.2012.6364244","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE International Conference on Communications (ICC)","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/A5052173154","display_name":"Eric Rebeiz","orcid":null},"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":"Eric Rebeiz","raw_affiliation_strings":["University of California, Los Angeles, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035343569","display_name":"Varun Jain","orcid":"https://orcid.org/0000-0003-1846-6805"},"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":"Varun Jain","raw_affiliation_strings":["University of California, Los Angeles, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, 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":["University of California, Los Angeles, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I161318765"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1619","last_page":"1623"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T11210","display_name":"Mathematical Analysis and Transform Methods","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cyclostationary-process","display_name":"Cyclostationary process","score":0.9030437469482422},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.7573850154876709},{"id":"https://openalex.org/keywords/wideband","display_name":"Wideband","score":0.6782041788101196},{"id":"https://openalex.org/keywords/cognitive-radio","display_name":"Cognitive radio","score":0.6624559760093689},{"id":"https://openalex.org/keywords/nyquist\u2013shannon-sampling-theorem","display_name":"Nyquist\u2013Shannon sampling theorem","score":0.5994068384170532},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5901447534561157},{"id":"https://openalex.org/keywords/nyquist-rate","display_name":"Nyquist rate","score":0.5586627721786499},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5381447076797485},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5088822841644287},{"id":"https://openalex.org/keywords/signal-reconstruction","display_name":"Signal reconstruction","score":0.4954732656478882},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.45631465315818787},{"id":"https://openalex.org/keywords/nyquist-stability-criterion","display_name":"Nyquist stability criterion","score":0.4538026452064514},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.4366976022720337},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4221714735031128},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.42083922028541565},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.32886865735054016},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25377053022384644},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.25350096821784973},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.20121905207633972},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.1898198127746582},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1529134213924408},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.13722515106201172},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12599706649780273},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.08376747369766235}],"concepts":[{"id":"https://openalex.org/C178351263","wikidata":"https://www.wikidata.org/wiki/Q3922399","display_name":"Cyclostationary process","level":3,"score":0.9030437469482422},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.7573850154876709},{"id":"https://openalex.org/C2780202535","wikidata":"https://www.wikidata.org/wiki/Q4524457","display_name":"Wideband","level":2,"score":0.6782041788101196},{"id":"https://openalex.org/C149946192","wikidata":"https://www.wikidata.org/wiki/Q3235733","display_name":"Cognitive radio","level":3,"score":0.6624559760093689},{"id":"https://openalex.org/C288623","wikidata":"https://www.wikidata.org/wiki/Q679800","display_name":"Nyquist\u2013Shannon sampling theorem","level":2,"score":0.5994068384170532},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5901447534561157},{"id":"https://openalex.org/C65914096","wikidata":"https://www.wikidata.org/wiki/Q6273772","display_name":"Nyquist rate","level":4,"score":0.5586627721786499},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5381447076797485},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5088822841644287},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.4954732656478882},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.45631465315818787},{"id":"https://openalex.org/C7630364","wikidata":"https://www.wikidata.org/wiki/Q1756793","display_name":"Nyquist stability criterion","level":3,"score":0.4538026452064514},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.4366976022720337},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4221714735031128},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.42083922028541565},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.32886865735054016},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25377053022384644},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.25350096821784973},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.20121905207633972},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.1898198127746582},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1529134213924408},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.13722515106201172},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12599706649780273},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.08376747369766235},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icc.2012.6364244","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc.2012.6364244","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE International Conference on Communications (ICC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2037701747","https://openalex.org/W2103164394","https://openalex.org/W2115367764","https://openalex.org/W2120961178","https://openalex.org/W2123629701","https://openalex.org/W2141295130","https://openalex.org/W2147046564","https://openalex.org/W2160122636","https://openalex.org/W2164233625","https://openalex.org/W6676962915"],"related_works":["https://openalex.org/W1995912754","https://openalex.org/W2128489695","https://openalex.org/W2950579472","https://openalex.org/W2541626582","https://openalex.org/W2963728205","https://openalex.org/W3016502135","https://openalex.org/W2338477490","https://openalex.org/W2060549919","https://openalex.org/W1902850042","https://openalex.org/W1992316120"],"abstract_inverted_index":{"Detecting":[0],"the":[1,14,37,78,81,95,99],"presence":[2],"of":[3,17,80,94],"licensed":[4],"users":[5],"and":[6,87,113],"avoiding":[7],"interference":[8],"to":[9,13,47],"them":[10],"is":[11,34,43,105,118],"vital":[12],"proper":[15],"operation":[16],"a":[18,25,44,65,89,108,124,130],"Cognitive":[19],"Radio":[20],"(CR)":[21],"network.":[22],"Operating":[23],"in":[24,52],"wideband":[26,54],"channel":[27],"requires":[28],"high":[29],"Nyquist":[30,96],"sampling":[31,42,49,134],"rates,":[32],"which":[33,63],"limited":[35],"by":[36],"state-of-the-art":[38],"A/D":[39],"converters.":[40],"Compressive":[41],"promising":[45],"solution":[46,117],"reduce":[48],"rates":[50,135],"required":[51],"modern":[53],"communication":[55],"systems.":[56],"Among":[57],"various":[58],"signal":[59,66],"detectors,":[60],"feature":[61],"detectors":[62],"exploit":[64,77],"cyclostationarity":[67],"are":[68],"robust":[69],"against":[70],"noise":[71],"uncertainties.":[72],"In":[73],"this":[74],"paper,":[75],"we":[76],"sparsity":[79],"two-dimensional":[82],"spectral":[83],"correlation":[84],"function":[85],"(SCF),":[86],"propose":[88],"reduced":[90],"complexity":[91],"reconstruction":[92,103],"method":[93],"SCF":[97,139],"from":[98],"sub-Nyquist":[100],"samples.":[101],"The":[102],"optimization":[104],"formulated":[106],"as":[107],"regularized":[109],"least":[110],"squares":[111],"problem,":[112],"its":[114],"closed":[115],"form":[116],"derived.":[119],"We":[120],"show":[121],"that":[122,136],"for":[123],"given":[125],"spectrum":[126],"sparsity,":[127],"there":[128],"exists":[129],"lower":[131],"bound":[132],"on":[133],"allows":[137],"reliable":[138],"reconstruction.":[140]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":7},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":5},{"year":2013,"cited_by_count":5},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}