{"id":"https://openalex.org/W7118692743","doi":"https://doi.org/10.1109/vtc2025-fall65116.2025.11310482","title":"Radio Frequency Fingerprinting Identification for LoRa Using Deep Transfer Triplet Network","display_name":"Radio Frequency Fingerprinting Identification for LoRa Using Deep Transfer Triplet Network","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W7118692743","doi":"https://doi.org/10.1109/vtc2025-fall65116.2025.11310482"},"language":null,"primary_location":{"id":"doi:10.1109/vtc2025-fall65116.2025.11310482","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2025-fall65116.2025.11310482","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 102nd Vehicular Technology Conference (VTC2025-Fall)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5078466056","display_name":"J. Ye","orcid":null},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiao Ye","raw_affiliation_strings":["Xi’an Jiaotong University,Information and Communications Engineering,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi’an Jiaotong University,Information and Communications Engineering,China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100734239","display_name":"Dongyang Xu","orcid":"https://orcid.org/0000-0002-6401-0545"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongyang Xu","raw_affiliation_strings":["Xi’an Jiaotong University,Information and Communications Engineering,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi’an Jiaotong University,Information and Communications Engineering,China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069735221","display_name":"Pengxiang Qin","orcid":"https://orcid.org/0009-0001-8393-6992"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengxiang Qin","raw_affiliation_strings":["Xi’an Jiaotong University,Information and Communications Engineering,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi’an Jiaotong University,Information and Communications Engineering,China","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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":0.992900013923645,"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":0.992900013923645,"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/T10964","display_name":"Wireless Communication Security Techniques","score":0.0006000000284984708,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.0005000000237487257,"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/robustness","display_name":"Robustness (evolution)","score":0.692300021648407},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5281000137329102},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.5045999884605408},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.4844000041484833},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4803999960422516},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4577000141143799},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4562000036239624},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.43950000405311584},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4142000079154968},{"id":"https://openalex.org/keywords/radio-frequency-identification","display_name":"Radio-frequency identification","score":0.4088999927043915}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7728000283241272},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.692300021648407},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5281000137329102},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5178999900817871},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.5045999884605408},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.4844000041484833},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4803999960422516},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4577000141143799},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4562000036239624},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.43950000405311584},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4142000079154968},{"id":"https://openalex.org/C204222849","wikidata":"https://www.wikidata.org/wiki/Q104954","display_name":"Radio-frequency identification","level":2,"score":0.4088999927043915},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.3944999873638153},{"id":"https://openalex.org/C74064498","wikidata":"https://www.wikidata.org/wiki/Q3396184","display_name":"Radio frequency","level":2,"score":0.3619999885559082},{"id":"https://openalex.org/C557945733","wikidata":"https://www.wikidata.org/wiki/Q389772","display_name":"Data transmission","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3352999985218048},{"id":"https://openalex.org/C149946192","wikidata":"https://www.wikidata.org/wiki/Q3235733","display_name":"Cognitive radio","level":3,"score":0.33480000495910645},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.32739999890327454},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.3199999928474426},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3093000054359436},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3010999858379364},{"id":"https://openalex.org/C2988879021","wikidata":"https://www.wikidata.org/wiki/Q273623","display_name":"Radio channel","level":3,"score":0.28690001368522644},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2849000096321106},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2815999984741211},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.26350000500679016},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2583000063896179},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2531999945640564},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtc2025-fall65116.2025.11310482","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2025-fall65116.2025.11310482","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 102nd Vehicular Technology Conference (VTC2025-Fall)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7225698232650757,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320323230","display_name":"Xidian University","ror":"https://ror.org/05s92vm98"},{"id":"https://openalex.org/F4320330485","display_name":"State Key Laboratory of Integrated Services Networks","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2122158102","https://openalex.org/W2155886813","https://openalex.org/W2170895945","https://openalex.org/W2304380770","https://openalex.org/W2887280559","https://openalex.org/W2889741439","https://openalex.org/W2907410281","https://openalex.org/W2995117710","https://openalex.org/W3042612328","https://openalex.org/W3172135703","https://openalex.org/W3181667991","https://openalex.org/W3206629225","https://openalex.org/W4287887097"],"related_works":[],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"radio":[3,100],"frequency":[4,101],"fingerprinting":[5],"(RFF),":[6],"which":[7],"leverages":[8],"artificial":[9],"intelligence":[10],"(AI)":[11],"to":[12,42,47,84,104,171],"exploit":[13],"the":[14,31,52,70,118],"unique":[15],"hardware":[16],"characteristics":[17],"of":[18,36,57],"wireless":[19,37],"devices,":[20],"has":[21],"emerged":[22],"as":[23],"a":[24,66,112,164],"promising":[25],"method":[26],"for":[27],"device":[28],"identification.":[29],"However,":[30],"complex":[32],"and":[33,54,81,89,133,143],"time-varying":[34,147],"nature":[35],"channels":[38],"makes":[39],"it":[40],"difficult":[41],"acquire":[43],"large-scale":[44],"datasets,":[45],"leading":[46],"small-sample":[48],"scenarios":[49],"that":[50,116,137],"hinder":[51],"generalization":[53,120],"identification":[55],"performance":[56],"RFF":[58],"models.":[59],"To":[60],"address":[61],"these":[62],"challenges,":[63],"we":[64,128],"propose":[65],"novel":[67],"approach":[68],"called":[69],"deep":[71],"transfer":[72,114],"triplet":[73,79],"network":[74],"(DTTN).":[75],"Specifically,":[76],"DTTN":[77,110,138,162],"integrates":[78],"loss":[80,83],"cross-entropy":[82],"simultaneously":[85],"enhance":[86],"feature":[87],"discriminability":[88],"robustness":[90,144],"by":[91],"leveraging":[92],"sample":[93],"similarity":[94],"while":[95,150],"mitigating":[96],"channel-induced":[97],"distortions":[98],"in":[99,145,167],"features.":[102],"Furthermore,":[103],"cope":[105],"with":[106],"data":[107],"distribution":[108],"shifts,":[109],"incorporates":[111],"parameter":[113],"strategy":[115],"improves":[117,140],"model\u2019s":[119],"across":[121],"varying":[122],"channel":[123,148],"conditions.":[124],"For":[125],"efficient":[126],"deployment,":[127],"evaluate":[129],"several":[130],"quantization":[131],"schemes":[132],"experimental":[134],"results":[135],"show":[136],"significantly":[139],"recognition":[141,168],"accuracy":[142,169],"small-sample,":[146],"scenarios,":[149],"also":[151],"supporting":[152],"lightweight":[153],"model":[154],"deployment.":[155],"With":[156],"approximately":[157],"100":[158],"samples":[159],"per":[160],"client,":[161],"achieves":[163],"13%":[165],"improvement":[166],"compared":[170],"conventional":[172],"convolutional":[173],"neural":[174],"networks":[175],"based":[176],"methods.":[177]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-01-08T00:00:00"}