{"id":"https://openalex.org/W4400727200","doi":"https://doi.org/10.1109/iwcmc61514.2024.10592537","title":"A New Radio Frequency Fingerprint Identification Method Based on CEEMDAN","display_name":"A New Radio Frequency Fingerprint Identification Method Based on CEEMDAN","publication_year":2024,"publication_date":"2024-05-27","ids":{"openalex":"https://openalex.org/W4400727200","doi":"https://doi.org/10.1109/iwcmc61514.2024.10592537"},"language":"en","primary_location":{"id":"doi:10.1109/iwcmc61514.2024.10592537","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc61514.2024.10592537","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Wireless Communications and Mobile Computing (IWCMC)","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/A5111886185","display_name":"Weikang Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weikang Yang","raw_affiliation_strings":["Southeast University,National Mobile Communications Research Laboratory,Nanjing,China"],"affiliations":[{"raw_affiliation_string":"Southeast University,National Mobile Communications Research Laboratory,Nanjing,China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107748060","display_name":"Jianing Zhao","orcid":"https://orcid.org/0000-0002-1579-0522"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianing Zhao","raw_affiliation_strings":["Southeast University,National Mobile Communications Research Laboratory,Nanjing,China"],"affiliations":[{"raw_affiliation_string":"Southeast University,National Mobile Communications Research Laboratory,Nanjing,China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104288754","display_name":"Shaoxuan Xue","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaoxuan Xue","raw_affiliation_strings":["Southeast University,National Mobile Communications Research Laboratory,Nanjing,China"],"affiliations":[{"raw_affiliation_string":"Southeast University,National Mobile Communications Research Laboratory,Nanjing,China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107952835","display_name":"Xin Wang","orcid":"https://orcid.org/0009-0009-0444-1704"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Wang","raw_affiliation_strings":["Southeast University,National Mobile Communications Research Laboratory,Nanjing,China"],"affiliations":[{"raw_affiliation_string":"Southeast University,National Mobile Communications Research Laboratory,Nanjing,China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113292450","display_name":"Xuenan Ni","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuenan Ni","raw_affiliation_strings":["Southeast University,National Mobile Communications Research Laboratory,Nanjing,China"],"affiliations":[{"raw_affiliation_string":"Southeast University,National Mobile Communications Research Laboratory,Nanjing,China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5111886185"],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":0.3894,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.54069187,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"109","last_page":"113"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.9864000082015991,"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"}},"topics":[{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.9864000082015991,"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/fingerprint","display_name":"Fingerprint (computing)","score":0.681629478931427},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.600691020488739},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5391469597816467},{"id":"https://openalex.org/keywords/fingerprint-recognition","display_name":"Fingerprint recognition","score":0.526098370552063},{"id":"https://openalex.org/keywords/radio-frequency-identification","display_name":"Radio-frequency identification","score":0.47866758704185486},{"id":"https://openalex.org/keywords/radio-frequency","display_name":"Radio frequency","score":0.441409170627594},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38578134775161743},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36839282512664795},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3358609974384308},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1813410520553589},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.0775260329246521}],"concepts":[{"id":"https://openalex.org/C2777826928","wikidata":"https://www.wikidata.org/wiki/Q3745713","display_name":"Fingerprint (computing)","level":2,"score":0.681629478931427},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.600691020488739},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5391469597816467},{"id":"https://openalex.org/C168406668","wikidata":"https://www.wikidata.org/wiki/Q178022","display_name":"Fingerprint recognition","level":3,"score":0.526098370552063},{"id":"https://openalex.org/C204222849","wikidata":"https://www.wikidata.org/wiki/Q104954","display_name":"Radio-frequency identification","level":2,"score":0.47866758704185486},{"id":"https://openalex.org/C74064498","wikidata":"https://www.wikidata.org/wiki/Q3396184","display_name":"Radio frequency","level":2,"score":0.441409170627594},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38578134775161743},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36839282512664795},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3358609974384308},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1813410520553589},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0775260329246521},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"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/iwcmc61514.2024.10592537","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc61514.2024.10592537","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Wireless Communications and Mobile Computing (IWCMC)","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":19,"referenced_works":["https://openalex.org/W1997651606","https://openalex.org/W2000093279","https://openalex.org/W2000982976","https://openalex.org/W2007221293","https://openalex.org/W2101397645","https://openalex.org/W2108286220","https://openalex.org/W2114755071","https://openalex.org/W2115449888","https://openalex.org/W2120390927","https://openalex.org/W2125056386","https://openalex.org/W2153635508","https://openalex.org/W2164162938","https://openalex.org/W2292953767","https://openalex.org/W2304380770","https://openalex.org/W2886145918","https://openalex.org/W4214713481","https://openalex.org/W4280543295","https://openalex.org/W4308655556","https://openalex.org/W6637139999"],"related_works":["https://openalex.org/W3014822659","https://openalex.org/W4362496757","https://openalex.org/W2566091814","https://openalex.org/W4389371618","https://openalex.org/W2051501574","https://openalex.org/W2117826006","https://openalex.org/W2114937328","https://openalex.org/W2148654711","https://openalex.org/W2608025327","https://openalex.org/W1621827506"],"abstract_inverted_index":{"Radio":[0],"Frequency":[1],"Fingerprint":[2],"Identification":[3],"(RFFI)":[4],"is":[5,78],"an":[6],"emerging":[7],"technology":[8],"for":[9,137],"device":[10],"identification":[11,133],"that":[12,102],"relies":[13],"on":[14,27],"the":[15,39,94,103,118,132,141,155],"emitter-specific":[16],"inherent":[17],"impairments.":[18],"This":[19],"paper":[20],"presents":[21],"a":[22,42],"new":[23],"RFFI":[24],"method":[25,105],"based":[26],"Complete":[28],"Ensemble":[29],"Empirical":[30],"Mode":[31],"Decomposition":[32],"with":[33,52],"Adaptive":[34],"Noise":[35],"(CEEMDAN).":[36],"CEEMDAN":[37,58,112],"decomposes":[38],"signal":[40],"into":[41],"collection":[43],"of":[44,96,111,143],"intrinsic":[45],"mode":[46],"functions":[47],"(IMFs)":[48],"accurately.":[49],"Signals":[50],"carrying":[51],"emitter":[53],"fingerprints":[54],"are":[55,62,87,128],"decomposed":[56],"by":[57],"and":[59,71,84,90],"fingerprint":[60,115],"features":[61,116],"extracted":[63],"after":[64],"Hilbert":[65,119],"Transformation,":[66],"namely":[67],"energy":[68],"entropy,":[69],"first-order":[70],"second-order":[72],"moment.":[73],"Support":[74],"Vector":[75],"Machine":[76],"(SVM)":[77],"used":[79],"as":[80],"classifier.":[81],"The":[82,109],"training":[83],"test":[85],"sets":[86],"randomly":[88],"disrupted":[89],"divided":[91],"according":[92],"to":[93,129,146],"ratio":[95],"8:":[97],"2.":[98],"Simulation":[99],"results":[100],"show":[101],"proposed":[104],"has":[106],"excellent":[107],"performance.":[108],"utilization":[110],"before":[113],"extracting":[114],"from":[117],"spectrum":[120],"greatly":[121],"enhances":[122],"their":[123],"distinguishability.":[124],"when":[125],"3":[126],"emitters":[127,144],"be":[130],"recognized,":[131],"accuracy":[134],"attains":[135],"95%":[136],"SNR=9":[138],"dB.":[139],"When":[140],"number":[142],"up":[145],"5,":[147],"it":[148],"reaches":[149],"85%":[150],"under":[151],"12":[152],"dB":[153],"in":[154],"additive":[156],"white":[157],"Gaussian":[158],"noise":[159],"(AWGN)":[160],"channel.":[161]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}
