{"id":"https://openalex.org/W4292873685","doi":"https://doi.org/10.1109/mwscas54063.2022.9859498","title":"Dual-Layer Waveform Domain Deep Learning Approach for RF Fingerprinting","display_name":"Dual-Layer Waveform Domain Deep Learning Approach for RF Fingerprinting","publication_year":2022,"publication_date":"2022-08-07","ids":{"openalex":"https://openalex.org/W4292873685","doi":"https://doi.org/10.1109/mwscas54063.2022.9859498"},"language":"en","primary_location":{"id":"doi:10.1109/mwscas54063.2022.9859498","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mwscas54063.2022.9859498","pdf_url":null,"source":{"id":"https://openalex.org/S4363606568","display_name":"2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS)","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/A5033850468","display_name":"Fredo Chavez","orcid":"https://orcid.org/0000-0002-5046-6832"},"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":"Fredo Chavez","raw_affiliation_strings":["Macquarie University,School of Engineering,Sydney,NSW,Australia","School of Engineering, Macquarie University, Sydney, NSW, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Macquarie University,School of Engineering,Sydney,NSW,Australia","institution_ids":["https://openalex.org/I99043593"]},{"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/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":["Macquarie University,School of Engineering,Sydney,NSW,Australia","School of Engineering, Macquarie University, Sydney, NSW, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Macquarie University,School of Engineering,Sydney,NSW,Australia","institution_ids":["https://openalex.org/I99043593"]},{"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":["Macquarie University,School of Engineering,Sydney,NSW,Australia","School of Engineering, Macquarie University, Sydney, NSW, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Macquarie University,School of Engineering,Sydney,NSW,Australia","institution_ids":["https://openalex.org/I99043593"]},{"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":0.4153,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.57720647,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"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/T10891","display_name":"Radar Systems and Signal Processing","score":0.9794999957084656,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T12357","display_name":"Digital Media Forensic Detection","score":0.9751999974250793,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.7921216487884521},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6819102764129639},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5710736513137817},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.5650404691696167},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.5194534659385681},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47136226296424866},{"id":"https://openalex.org/keywords/wireless-network","display_name":"Wireless network","score":0.4336507320404053},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42632368206977844},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.4209184944629669},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.4181044399738312},{"id":"https://openalex.org/keywords/radio-frequency","display_name":"Radio frequency","score":0.4166979193687439},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3857424855232239},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3807348906993866},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.19137799739837646},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.1085435152053833},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.09235742688179016}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7921216487884521},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6819102764129639},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5710736513137817},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.5650404691696167},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.5194534659385681},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47136226296424866},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.4336507320404053},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42632368206977844},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.4209184944629669},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4181044399738312},{"id":"https://openalex.org/C74064498","wikidata":"https://www.wikidata.org/wiki/Q3396184","display_name":"Radio frequency","level":2,"score":0.4166979193687439},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3857424855232239},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3807348906993866},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.19137799739837646},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.1085435152053833},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.09235742688179016}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mwscas54063.2022.9859498","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mwscas54063.2022.9859498","pdf_url":null,"source":{"id":"https://openalex.org/S4363606568","display_name":"2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1985671681","https://openalex.org/W2109631601","https://openalex.org/W2169528858","https://openalex.org/W2590333464","https://openalex.org/W2774361072","https://openalex.org/W2791256362","https://openalex.org/W2889741439","https://openalex.org/W2900248381","https://openalex.org/W2929757916","https://openalex.org/W2968197850","https://openalex.org/W2979406270","https://openalex.org/W2979712962","https://openalex.org/W2980132432","https://openalex.org/W3004340854","https://openalex.org/W3015448029","https://openalex.org/W3134326557","https://openalex.org/W3168654631"],"related_works":["https://openalex.org/W4312417841","https://openalex.org/W4321369474","https://openalex.org/W2738221750","https://openalex.org/W2732542196","https://openalex.org/W564581980","https://openalex.org/W4295190261","https://openalex.org/W4214561993","https://openalex.org/W2763109982","https://openalex.org/W3193301557","https://openalex.org/W4320802194"],"abstract_inverted_index":{"The":[0,90,139],"widespread":[1],"deployment":[2],"of":[3,13,16,26,42,47,92,119,159],"wireless":[4,27],"sensors":[5],"and":[6,22,102,127],"devices":[7,115],"is":[8,96,132],"enabling":[9],"the":[10,14,20,23,45,66,93,107,130,144,157],"rapid":[11],"development":[12],"Internet":[15],"Things.":[17],"To":[18],"address":[19],"privacy":[21],"security":[24],"issues":[25],"transmissions,":[28],"Radio":[29],"Frequency":[30],"(RF)":[31],"fingerprinting":[32],"techniques":[33],"can":[34,71],"be":[35,72],"used":[36],"to":[37,111,136],"provide":[38],"an":[39,117],"additional":[40],"layer":[41],"protection.":[43],"With":[44],"emergence":[46],"deep":[48],"learning":[49],"solutions":[50],"for":[51,87],"identifying":[52],"devices,":[53],"we":[54],"propose":[55],"a":[56,160],"pre-processing":[57,146],"approach":[58,95,108,147],"that":[59,143],"generates":[60],"dual-layer":[61],"waveform":[62],"domain":[63],"images":[64],"from":[65],"captured":[67,100],"raw":[68],"I/Q-samples":[69],"which":[70],"combined":[73,104],"with":[74,105,116,153],"either":[75],"Multilayer":[76],"Perceptron":[77],"Neural":[78,83],"Network":[79,84],"(MLPNN)":[80],"or":[81],"Convolutional":[82],"(CNN)":[85],"architectures":[86],"RF":[88],"fingerprinting.":[89],"performance":[91],"proposed":[94,145],"evaluated":[97],"using":[98],"over-the-air":[99],"data,":[101],"when":[103,129],"CNN":[106,161],"was":[109],"able":[110],"identify":[112],"12":[113],"Zigbee":[114],"accuracy":[118],"99%":[120],"at":[121],"24":[122,137],"dB":[123],"Signal-to-Noise":[124],"Ratio":[125],"(SNR),":[126],"89%":[128],"SNR":[131],"varied":[133],"between":[134],"16":[135],"dB.":[138],"experimental":[140],"results":[141,148],"show":[142],"in":[149],"reduced":[150],"training":[151],"time":[152],"minimal":[154],"impact":[155],"on":[156],"complexity":[158],"model.":[162]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}