{"id":"https://openalex.org/W2991378813","doi":"https://doi.org/10.1109/pimrc.2019.8904422","title":"Long Short-Term Memory based Spectrum Sensing Scheme for Cognitive Radio","display_name":"Long Short-Term Memory based Spectrum Sensing Scheme for Cognitive Radio","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2991378813","doi":"https://doi.org/10.1109/pimrc.2019.8904422","mag":"2991378813"},"language":"en","primary_location":{"id":"doi:10.1109/pimrc.2019.8904422","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pimrc.2019.8904422","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","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/A5091481633","display_name":"Nikhil Balwani","orcid":null},"institutions":[{"id":"https://openalex.org/I52527943","display_name":"Ahmedabad University","ror":"https://ror.org/02swff503","country_code":"IN","type":"education","lineage":["https://openalex.org/I52527943"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Nikhil Balwani","raw_affiliation_strings":["School of Engineering and Applied Science, Ahmedabad University, India"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Applied Science, Ahmedabad University, India","institution_ids":["https://openalex.org/I52527943"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101629691","display_name":"Dhaval K. Patel","orcid":"https://orcid.org/0000-0002-1350-4959"},"institutions":[{"id":"https://openalex.org/I52527943","display_name":"Ahmedabad University","ror":"https://ror.org/02swff503","country_code":"IN","type":"education","lineage":["https://openalex.org/I52527943"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Dhaval K. Patel","raw_affiliation_strings":["School of Engineering and Applied Science, Ahmedabad University, India"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Applied Science, Ahmedabad University, India","institution_ids":["https://openalex.org/I52527943"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021684266","display_name":"Brijesh Soni","orcid":"https://orcid.org/0000-0002-0362-5087"},"institutions":[{"id":"https://openalex.org/I52527943","display_name":"Ahmedabad University","ror":"https://ror.org/02swff503","country_code":"IN","type":"education","lineage":["https://openalex.org/I52527943"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Brijesh Soni","raw_affiliation_strings":["School of Engineering and Applied Science, Ahmedabad University, India"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Applied Science, Ahmedabad University, India","institution_ids":["https://openalex.org/I52527943"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050222424","display_name":"Miguel L\u00f3pez\u2010Ben\u00edtez","orcid":"https://orcid.org/0000-0003-0526-6687"},"institutions":[{"id":"https://openalex.org/I146655781","display_name":"University of Liverpool","ror":"https://ror.org/04xs57h96","country_code":"GB","type":"education","lineage":["https://openalex.org/I146655781"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Miguel Lopez-Benitez","raw_affiliation_strings":["Department of Electrical Engineering and Electronics, University of Liverpool, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Electronics, University of Liverpool, United Kingdom","institution_ids":["https://openalex.org/I146655781"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5091481633"],"corresponding_institution_ids":["https://openalex.org/I52527943"],"apc_list":null,"apc_paid":null,"fwci":1.8571,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.8739133,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"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":0.9998999834060669,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9894999861717224,"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":0.986299991607666,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cognitive-radio","display_name":"Cognitive radio","score":0.8471195697784424},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7740687727928162},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.7165958285331726},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5778006315231323},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5406396389007568},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.5197686553001404},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5076471567153931},{"id":"https://openalex.org/keywords/spectrum","display_name":"Spectrum (functional analysis)","score":0.4747788608074188},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46757060289382935},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.4563380777835846},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4212869703769684},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3249935507774353},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.18084096908569336},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.14902657270431519},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.13131433725357056},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.12182208895683289}],"concepts":[{"id":"https://openalex.org/C149946192","wikidata":"https://www.wikidata.org/wiki/Q3235733","display_name":"Cognitive radio","level":3,"score":0.8471195697784424},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7740687727928162},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.7165958285331726},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5778006315231323},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5406396389007568},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.5197686553001404},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5076471567153931},{"id":"https://openalex.org/C156778621","wikidata":"https://www.wikidata.org/wiki/Q1365748","display_name":"Spectrum (functional analysis)","level":2,"score":0.4747788608074188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46757060289382935},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.4563380777835846},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4212869703769684},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3249935507774353},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.18084096908569336},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.14902657270431519},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.13131433725357056},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.12182208895683289},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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/pimrc.2019.8904422","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pimrc.2019.8904422","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","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":24,"referenced_works":["https://openalex.org/W1488270586","https://openalex.org/W1598796236","https://openalex.org/W1984835905","https://openalex.org/W1993573682","https://openalex.org/W2058074751","https://openalex.org/W2064675550","https://openalex.org/W2071707134","https://openalex.org/W2101734002","https://openalex.org/W2101840010","https://openalex.org/W2125161808","https://openalex.org/W2149256602","https://openalex.org/W2155309582","https://openalex.org/W2172139273","https://openalex.org/W2293634267","https://openalex.org/W2497774009","https://openalex.org/W2562947506","https://openalex.org/W2741230443","https://openalex.org/W2773994609","https://openalex.org/W2786168957","https://openalex.org/W2795250443","https://openalex.org/W2885642955","https://openalex.org/W3104028856","https://openalex.org/W6635679246","https://openalex.org/W6696934422"],"related_works":["https://openalex.org/W2559261346","https://openalex.org/W4280609833","https://openalex.org/W4235820682","https://openalex.org/W185479762","https://openalex.org/W2775301649","https://openalex.org/W2048427509","https://openalex.org/W4233632157","https://openalex.org/W2330895226","https://openalex.org/W2994439156","https://openalex.org/W2587869769"],"abstract_inverted_index":{"The":[0,67,81],"application":[1],"of":[2,19,77,94,140],"machine":[3,89],"learning":[4,90],"models":[5,21],"to":[6,23,63],"spectrum":[7,38,106],"sensing":[8,53,61,69,107],"in":[9,15,27,92,147],"cognitive":[10],"radio":[11,79],"is":[12,40,55,71,85,101,129,135],"not":[13],"uncommon":[14],"literature,":[16],"but":[17],"most":[18],"these":[20],"fail":[22],"consider":[24],"temporal":[25,34],"dependencies":[26],"the":[28,33,37,51,59,64,98,112,116,132,138],"signal.":[29],"In":[30],"this":[31],"paper,":[32],"correlation":[35],"among":[36],"data":[39,76],"exploited":[41],"using":[42],"a":[43],"Long":[44],"Short-Term":[45],"Memory":[46],"(LSTM)":[47],"network.":[48],"More":[49],"specifically,":[50],"previous":[52],"event":[54,62],"fed":[56],"along":[57],"with":[58,87,104],"present":[60],"LSTM":[65,83],"model.":[66],"proposed":[68,82,99,113],"scheme":[70,100,114],"validated":[72],"based":[73],"on":[74],"empirical":[75],"various":[78],"technologies.":[80],"model":[84],"compared":[86,103],"other":[88,105],"algorithms":[91],"terms":[93],"classification":[95,120],"accuracy.":[96],"Furthermore,":[97],"also":[102],"techniques.":[108],"Results":[109],"indicate":[110],"that":[111,131],"improves":[115],"detection":[117],"performance":[118],"and":[119,144],"accuracy":[121],"at":[122,137],"low":[123],"signal-to-noise":[124],"ratio":[125],"regimes.":[126],"Moreover,":[127],"it":[128],"observed":[130],"achieved":[133],"improvement":[134],"obtained":[136],"expense":[139],"longer":[141],"training":[142],"time":[143],"nominal":[145],"increase":[146],"execution":[148],"time.":[149]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-04-14T08:04:32.555800","created_date":"2025-10-10T00:00:00"}
