{"id":"https://openalex.org/W4317419060","doi":"https://doi.org/10.1109/vtc2022-fall57202.2022.10013065","title":"Embrace Imperfect Datasets: New Time Representation for RFF Identification","display_name":"Embrace Imperfect Datasets: New Time Representation for RFF Identification","publication_year":2022,"publication_date":"2022-09-01","ids":{"openalex":"https://openalex.org/W4317419060","doi":"https://doi.org/10.1109/vtc2022-fall57202.2022.10013065"},"language":"en","primary_location":{"id":"doi:10.1109/vtc2022-fall57202.2022.10013065","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/vtc2022-fall57202.2022.10013065","pdf_url":null,"source":{"id":"https://openalex.org/S4363607792","display_name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","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 96th Vehicular Technology Conference (VTC2022-Fall)","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/A5022328727","display_name":"Xinyu Qi","orcid":"https://orcid.org/0000-0002-5304-2170"},"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":"Xinyu Qi","raw_affiliation_strings":["Southeast University,School of Cyber Science and Engineering,Nanjing,China","School of Cyber Science and Engineering, Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Southeast University,School of Cyber Science and Engineering,Nanjing,China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"School of Cyber Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075678549","display_name":"Aiqun Hu","orcid":"https://orcid.org/0000-0002-0398-4899"},"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"]},{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aiqun Hu","raw_affiliation_strings":["Southeast University,School of Information Science and Engineering,Nanjing,China","School of Information Science and Engineering, Southeast University, Nanjing, China","National Mobile Communications Research Laboratory, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Southeast University,School of Information Science and Engineering,Nanjing,China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"School of Information Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"National Mobile Communications Research Laboratory, Nanjing, China","institution_ids":["https://openalex.org/I180662265"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5022328727"],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19409578,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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":0.9994999766349792,"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":0.9994999766349792,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9905999898910522,"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"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.953000009059906,"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/computer-science","display_name":"Computer science","score":0.7464547753334045},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5608002543449402},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4984903335571289},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.49430251121520996},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47213202714920044},{"id":"https://openalex.org/keywords/preamble","display_name":"Preamble","score":0.4646572172641754},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4631326496601105},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4540695250034332},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44354841113090515},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.41926640272140503},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4021632671356201},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3894442915916443},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35933759808540344},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3549041152000427},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.33709612488746643},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.16795289516448975}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7464547753334045},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5608002543449402},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4984903335571289},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.49430251121520996},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47213202714920044},{"id":"https://openalex.org/C2780691134","wikidata":"https://www.wikidata.org/wiki/Q1129448","display_name":"Preamble","level":3,"score":0.4646572172641754},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4631326496601105},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4540695250034332},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44354841113090515},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.41926640272140503},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4021632671356201},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3894442915916443},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35933759808540344},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3549041152000427},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33709612488746643},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.16795289516448975},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtc2022-fall57202.2022.10013065","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/vtc2022-fall57202.2022.10013065","pdf_url":null,"source":{"id":"https://openalex.org/S4363607792","display_name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","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 96th Vehicular Technology Conference (VTC2022-Fall)","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":18,"referenced_works":["https://openalex.org/W1966554111","https://openalex.org/W2010481082","https://openalex.org/W2026430219","https://openalex.org/W2052476756","https://openalex.org/W2081028405","https://openalex.org/W2081681829","https://openalex.org/W2154170045","https://openalex.org/W2734885106","https://openalex.org/W2791256362","https://openalex.org/W2803989302","https://openalex.org/W2890720923","https://openalex.org/W2898104747","https://openalex.org/W2898190325","https://openalex.org/W2938144699","https://openalex.org/W2942067158","https://openalex.org/W3127763944","https://openalex.org/W6675357634","https://openalex.org/W6683549911"],"related_works":["https://openalex.org/W3131553538","https://openalex.org/W2369766703","https://openalex.org/W3204754495","https://openalex.org/W2392460115","https://openalex.org/W4402159933","https://openalex.org/W2391859761","https://openalex.org/W4402160580","https://openalex.org/W2893442187","https://openalex.org/W2964954556","https://openalex.org/W3088721469"],"abstract_inverted_index":{"As":[0],"the":[1,19,32,85,88,91,95,122,140,157,160],"inherent":[2],"attribute":[3],"of":[4,18,23,46,87,97,103,156,163],"equipment":[5],"circuit":[6],"hardware,":[7],"Radio":[8],"Frequency":[9],"Fingerprints":[10],"(RFFs)":[11],"is":[12,114],"hardly-forged":[13],"and":[14,35,48,100,151],"has":[15],"become":[16],"one":[17],"most":[20],"powerful":[21],"guarantees":[22],"physical":[24],"layer":[25],"security.":[26],"Most":[27],"existing":[28],"RFF-based":[29],"methods":[30],"ignore":[31],"temporal":[33],"relation":[34],"are":[36,130],"designed":[37,135],"under":[38],"an":[39],"ideal":[40],"dataset":[41],"with":[42,132,146,153],"a":[43,68,101,108,154],"large":[44],"number":[45],"samples":[47],"complete":[49],"signal":[50,75,89],"records,":[51],"thus":[52],"they":[53],"tend":[54],"to":[55,116],"be":[56],"less":[57],"versatile":[58],"in":[59],"real-world":[60],"scenarios.":[61],"To":[62],"address":[63],"this":[64],"problem,":[65],"we":[66],"propose":[67],"novel":[69],"time":[70,92],"representation":[71,102],"method":[72],"for":[73,124],"wireless":[74],"pictorialization":[76],"called":[77],"modified":[78],"gramian":[79],"angular":[80],"fields":[81],"(MGAF),":[82],"which":[83],"depicts":[84],"characteristics":[86],"along":[90],"axis":[93],"through":[94],"transformation":[96],"coordinate":[98],"system":[99],"trigonometric":[104],"difference.":[105],"After":[106],"that,":[107],"channel-selectable":[109],"convolution":[110],"neural":[111],"network":[112],"(CNN)":[113],"used":[115],"extract":[117],"high-dimensional":[118],"feature":[119],"vectors":[120],"as":[121],"RFFs":[123],"further":[125],"identification.":[126],"The":[127,137],"entire":[128],"experiments":[129],"conducted":[131],"purposely":[133],"poorly":[134],"datasets.":[136],"results":[138],"shows":[139],"accuracy":[141],"can":[142],"reach":[143],"at":[144,159],"94.82%":[145],"only":[147],"three":[148],"half-sine":[149],"waves":[150],"99.26%":[152],"quarter":[155],"preamble":[158],"SNR":[161],"level":[162],"30":[164],"dB.":[165]},"counts_by_year":[],"updated_date":"2025-12-21T23:12:01.093139","created_date":"2025-10-10T00:00:00"}
