{"id":"https://openalex.org/W3134326557","doi":"https://doi.org/10.1109/iscas51556.2021.9401486","title":"Waveform Domain Deep Learning Approach for RF Fingerprinting","display_name":"Waveform Domain Deep Learning Approach for RF Fingerprinting","publication_year":2021,"publication_date":"2021-04-27","ids":{"openalex":"https://openalex.org/W3134326557","doi":"https://doi.org/10.1109/iscas51556.2021.9401486","mag":"3134326557"},"language":"en","primary_location":{"id":"doi:10.1109/iscas51556.2021.9401486","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas51556.2021.9401486","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Symposium on Circuits and Systems (ISCAS)","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/A5100374360","display_name":"Bo Li","orcid":"https://orcid.org/0000-0001-6709-0942"},"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":"Bo Li","raw_affiliation_strings":["School of Engineering, Macquarie University, Sydney, NSW, Australia"],"raw_orcid":null,"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/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":null,"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":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.2389,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.89756629,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10767","display_name":"Advanced Photonic Communication Systems","score":0.9855999946594238,"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/T12791","display_name":"Full-Duplex Wireless Communications","score":0.9830999970436096,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7837567329406738},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.6392804980278015},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.5835459232330322},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.5796913504600525},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5731288194656372},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5434340834617615},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49961280822753906},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4553329348564148},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4288948178291321},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.38769620656967163},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34857800602912903},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.18946778774261475},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.09568113088607788}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7837567329406738},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.6392804980278015},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.5835459232330322},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.5796913504600525},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5731288194656372},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5434340834617615},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49961280822753906},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4553329348564148},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4288948178291321},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.38769620656967163},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34857800602912903},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.18946778774261475},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.09568113088607788},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscas51556.2021.9401486","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas51556.2021.9401486","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Symposium on Circuits and Systems (ISCAS)","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":12,"referenced_works":["https://openalex.org/W1874399837","https://openalex.org/W2012166391","https://openalex.org/W2058401212","https://openalex.org/W2105103777","https://openalex.org/W2134295053","https://openalex.org/W2155886813","https://openalex.org/W2273675851","https://openalex.org/W2317978897","https://openalex.org/W2791256362","https://openalex.org/W2889741439","https://openalex.org/W2982637733","https://openalex.org/W2999798127"],"related_works":["https://openalex.org/W96612179","https://openalex.org/W2770234245","https://openalex.org/W2987774938","https://openalex.org/W2566006169","https://openalex.org/W4256492088","https://openalex.org/W632915154","https://openalex.org/W4229499248","https://openalex.org/W1974895211","https://openalex.org/W4378874356","https://openalex.org/W4236726576"],"abstract_inverted_index":{"With":[0],"the":[1,9,51,58,62,113,126,129,132,156,163,177],"widespread":[2],"deployment":[3],"of":[4,12,30,38,68,104,115,121,131,162],"wireless":[5,26],"sensor":[6],"networks":[7],"and":[8,22,35,45,64,128,149],"nascent":[10],"Internet":[11],"Things":[13],"(IoT),":[14],"enabling":[15],"devices":[16],"to":[17,60,108],"be":[18],"connected":[19],"in":[20],"wider,":[21],"denser":[23],"ecosystems,":[24],"improved":[25],"security":[27,63],"has":[28,57],"become":[29],"paramount":[31],"importance.":[32],"Limited":[33],"power":[34],"computational":[36],"resources":[37],"these":[39],"devices,":[40,175],"however,":[41],"render":[42],"sophisticated":[43],"algorithms":[44],"protocols":[46],"not":[47],"suitable":[48],"for":[49,99],"all":[50],"devices.":[52],"Radio":[53],"Frequency":[54],"(RF)":[55],"fingerprinting":[56,72],"potential":[59],"enhance":[61],"with":[65,77,176],"increasing":[66],"popularity":[67],"deep":[69,134],"learning,":[70],"RF":[71],"approaches":[73],"have":[74],"attracted":[75],"attention":[76],"new":[78],"techniques":[79],"proposed.":[80],"In":[81],"this":[82],"paper":[83],"we":[84],"present":[85],"a":[86,139],"novel":[87],"waveform":[88,152],"domain-based":[89],"approach":[90,165,179],"operating":[91],"on":[92,151],"images":[93,153],"generated":[94,154],"from":[95,117,155,172],"captured":[96,157,171],"raw":[97,109,122,158],"samples":[98,123],"device":[100],"identification.":[101],"The":[102,160],"use":[103,138],"images,":[105],"as":[106],"opposed":[107],"sample":[110],"sequences,":[111],"enables":[112],"capture":[114],"information":[116],"theoretically":[118],"infinite":[119],"number":[120],"without":[124],"impacting":[125],"structure":[127],"complexity":[130],"subsequent":[133],"learning":[135],"processing.":[136],"We":[137],"simple":[140],"Dense":[141],"Neural":[142],"Network":[143],"(DNN)":[144],"model":[145],"which":[146],"is":[147,166],"implemented":[148],"trained":[150],"samples.":[159],"efficacy":[161],"proposed":[164,178],"demonstrated":[167],"using":[168],"over-the-air":[169],"signals":[170],"12":[173],"Zigbee":[174],"achieving":[180],"near":[181],"99%":[182],"identification":[183],"accuracy.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}