{"id":"https://openalex.org/W4283217641","doi":"https://doi.org/10.1109/infocomwkshps54753.2022.9798252","title":"Radio Frequency Fingerprint Identification Method Based on Ensemble Learning","display_name":"Radio Frequency Fingerprint Identification Method Based on Ensemble Learning","publication_year":2022,"publication_date":"2022-05-02","ids":{"openalex":"https://openalex.org/W4283217641","doi":"https://doi.org/10.1109/infocomwkshps54753.2022.9798252"},"language":"en","primary_location":{"id":"doi:10.1109/infocomwkshps54753.2022.9798252","is_oa":false,"landing_page_url":"https://doi.org/10.1109/infocomwkshps54753.2022.9798252","pdf_url":null,"source":{"id":"https://openalex.org/S4363607985","display_name":"IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","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":"IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","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/A5002101589","display_name":"Yu Huang","orcid":"https://orcid.org/0000-0002-7930-9056"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yu Huang","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications,College of Telecommunications and Information Engineering,Nanjing,China,210003"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications,College of Telecommunications and Information Engineering,Nanjing,China,210003","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100354999","display_name":"Pengfei Liu","orcid":"https://orcid.org/0000-0002-1603-3670"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengfei Liu","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications,College of Telecommunications and Information Engineering,Nanjing,China,210003"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications,College of Telecommunications and Information Engineering,Nanjing,China,210003","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101782559","display_name":"Jie Yang","orcid":"https://orcid.org/0000-0002-6156-2694"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Yang","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications,College of Telecommunications and Information Engineering,Nanjing,China,210003"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications,College of Telecommunications and Information Engineering,Nanjing,China,210003","institution_ids":["https://openalex.org/I41198531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5002101589"],"corresponding_institution_ids":["https://openalex.org/I41198531"],"apc_list":null,"apc_paid":null,"fwci":0.6233,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.66381168,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"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/T12131","display_name":"Wireless Signal Modulation Classification","score":1.0,"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":1.0,"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":0.9542999863624573,"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/T10767","display_name":"Advanced Photonic Communication Systems","score":0.9506000280380249,"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":0.7651031613349915},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.7332138419151306},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6882926821708679},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6150729656219482},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5727302432060242},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5690721869468689},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5641017556190491},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.5094917416572571},{"id":"https://openalex.org/keywords/radio-frequency","display_name":"Radio frequency","score":0.5021493434906006},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4658573269844055},{"id":"https://openalex.org/keywords/fingerprint","display_name":"Fingerprint (computing)","score":0.4539225101470947},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42422330379486084},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38431107997894287},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.16534185409545898},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07320719957351685}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7651031613349915},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7332138419151306},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6882926821708679},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6150729656219482},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5727302432060242},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5690721869468689},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5641017556190491},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.5094917416572571},{"id":"https://openalex.org/C74064498","wikidata":"https://www.wikidata.org/wiki/Q3396184","display_name":"Radio frequency","level":2,"score":0.5021493434906006},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4658573269844055},{"id":"https://openalex.org/C2777826928","wikidata":"https://www.wikidata.org/wiki/Q3745713","display_name":"Fingerprint (computing)","level":2,"score":0.4539225101470947},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42422330379486084},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38431107997894287},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.16534185409545898},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07320719957351685}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/infocomwkshps54753.2022.9798252","is_oa":false,"landing_page_url":"https://doi.org/10.1109/infocomwkshps54753.2022.9798252","pdf_url":null,"source":{"id":"https://openalex.org/S4363607985","display_name":"IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","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":"IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","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":32,"referenced_works":["https://openalex.org/W2006208855","https://openalex.org/W2010481082","https://openalex.org/W2012166391","https://openalex.org/W2108286220","https://openalex.org/W2145938544","https://openalex.org/W2155886813","https://openalex.org/W2407898405","https://openalex.org/W2606332932","https://openalex.org/W2908997014","https://openalex.org/W2929757916","https://openalex.org/W2990455952","https://openalex.org/W3003174479","https://openalex.org/W3004340854","https://openalex.org/W3006541201","https://openalex.org/W3007970939","https://openalex.org/W3011517489","https://openalex.org/W3013935691","https://openalex.org/W3023365694","https://openalex.org/W3035609320","https://openalex.org/W3049287738","https://openalex.org/W3088383832","https://openalex.org/W3123268700","https://openalex.org/W3146023480","https://openalex.org/W3159551017","https://openalex.org/W3167948099","https://openalex.org/W3170485195","https://openalex.org/W3189227796","https://openalex.org/W3197025184","https://openalex.org/W3205971195","https://openalex.org/W4205273399","https://openalex.org/W6681768502","https://openalex.org/W6757355660"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W4310224730","https://openalex.org/W2766514146","https://openalex.org/W4289703016","https://openalex.org/W2885516856","https://openalex.org/W4296079469"],"abstract_inverted_index":{"At":[0],"the":[1,4,25,36,42,51,59,66,88,92,98,119,126,155,165,170,174],"physical":[2],"layer,":[3],"combination":[5],"of":[6,54,62,69,94,101,114],"radio":[7],"frequency":[8],"(RF)":[9],"fingerprint":[10,79],"and":[11,65,116,129,146,163],"deep":[12],"learning":[13,56,71,151],"has":[14],"been":[15],"widely":[16],"used":[17],"to":[18,33,45,158],"identify":[19],"wireless":[20],"communication":[21],"devices.":[22],"Considering":[23,50],"that":[24],"traditional":[26,95,183],"convolutional":[27],"neural":[28],"network":[29],"(CNN)":[30],"is":[31,39,136,180],"applied":[32],"RF":[34,78,99],"fingerprint,":[35],"classification":[37,60,80,89,167,178],"performance":[38],"poor":[40],"in":[41,72,149,185],"low":[43,186],"signal":[44],"noise":[46],"ratio":[47],"(SRN)":[48],"scenario.":[49],"significant":[52],"effect":[53],"ensemble":[55,70,84,150],"on":[57,83,91],"improving":[58],"accuracy":[61,90,179],"base":[63,161],"classifier":[64],"wide":[67],"application":[68],"other":[73],"fields,":[74],"we":[75],"propose":[76],"an":[77,140],"method":[81],"based":[82],"learning,":[85],"which":[86],"improves":[87],"basis":[93],"CNN.":[96],"Firstly,":[97],"signals":[100,111],"four":[102],"power":[103],"amplifiers":[104],"are":[105,112,122,152],"collected":[106],"by":[107],"acquisition":[108],"equipment.":[109],"These":[110],"composed":[113],"in-phase":[115],"quadrature":[117],"signals,":[118],"sampling":[120],"points":[121],"200,000.":[123],"After":[124],"slicing":[125],"data":[127],"samples":[128],"artificially":[130],"introducing":[131],"different":[132],"SRN":[133],"noises,":[134],"it":[135],"then":[137],"input":[138],"into":[139],"improved":[141,156],"CNN":[142,157,184],"for":[143],"training.":[144],"Bagging":[145],"Boosting":[147],"algorithms":[148],"combined":[153],"with":[154],"integrate":[159],"multiple":[160],"classifiers":[162],"output":[164],"final":[166],"results.":[168],"Finally,":[169],"simulation":[171],"results":[172],"prove":[173],"proposed":[175],"method.":[176],"Its":[177],"better":[181],"than":[182],"SNR":[187],"environment.":[188]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}
