{"id":"https://openalex.org/W7131809723","doi":"https://doi.org/10.1109/access.2026.3669033","title":"Communication Specific Emitter Identification Based on Deep Convolutional Feature Matching","display_name":"Communication Specific Emitter Identification Based on Deep Convolutional Feature Matching","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7131809723","doi":"https://doi.org/10.1109/access.2026.3669033"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3669033","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3669033","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3669033","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jie Ren","orcid":"https://orcid.org/0000-0001-9256-0731"},"institutions":[{"id":"https://openalex.org/I194716290","display_name":"China Academy of Space Technology","ror":"https://ror.org/025397a59","country_code":"CN","type":"government","lineage":["https://openalex.org/I194716290","https://openalex.org/I2802615301"]},{"id":"https://openalex.org/I4210100278","display_name":"Beijing Satellite Navigation Center","ror":"https://ror.org/0188rvx33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210100278"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Ren","raw_affiliation_strings":["China, Academy of Space Technology, Institute of Telecommunication and Navigation Satellites, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9256-0731","affiliations":[{"raw_affiliation_string":"China, Academy of Space Technology, Institute of Telecommunication and Navigation Satellites, Beijing, China","institution_ids":["https://openalex.org/I194716290","https://openalex.org/I4210100278"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127367452","display_name":"Xiaoyang Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I194716290","display_name":"China Academy of Space Technology","ror":"https://ror.org/025397a59","country_code":"CN","type":"government","lineage":["https://openalex.org/I194716290","https://openalex.org/I2802615301"]},{"id":"https://openalex.org/I4210100278","display_name":"Beijing Satellite Navigation Center","ror":"https://ror.org/0188rvx33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210100278"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyang Chen","raw_affiliation_strings":["China, Academy of Space Technology, Institute of Telecommunication and Navigation Satellites, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China, Academy of Space Technology, Institute of Telecommunication and Navigation Satellites, Beijing, China","institution_ids":["https://openalex.org/I194716290","https://openalex.org/I4210100278"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100662583","display_name":"Ming Li","orcid":"https://orcid.org/0000-0002-4706-5173"},"institutions":[{"id":"https://openalex.org/I194716290","display_name":"China Academy of Space Technology","ror":"https://ror.org/025397a59","country_code":"CN","type":"government","lineage":["https://openalex.org/I194716290","https://openalex.org/I2802615301"]},{"id":"https://openalex.org/I4210100278","display_name":"Beijing Satellite Navigation Center","ror":"https://ror.org/0188rvx33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210100278"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Li","raw_affiliation_strings":["China, Academy of Space Technology, Institute of Telecommunication and Navigation Satellites, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China, Academy of Space Technology, Institute of Telecommunication and Navigation Satellites, Beijing, China","institution_ids":["https://openalex.org/I194716290","https://openalex.org/I4210100278"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072204746","display_name":"Shuai Li","orcid":"https://orcid.org/0000-0001-9873-6531"},"institutions":[{"id":"https://openalex.org/I194716290","display_name":"China Academy of Space Technology","ror":"https://ror.org/025397a59","country_code":"CN","type":"government","lineage":["https://openalex.org/I194716290","https://openalex.org/I2802615301"]},{"id":"https://openalex.org/I4210100278","display_name":"Beijing Satellite Navigation Center","ror":"https://ror.org/0188rvx33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210100278"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuai Li","raw_affiliation_strings":["China, Academy of Space Technology, Institute of Telecommunication and Navigation Satellites, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China, Academy of Space Technology, Institute of Telecommunication and Navigation Satellites, Beijing, China","institution_ids":["https://openalex.org/I194716290","https://openalex.org/I4210100278"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19593593,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"35266","last_page":"35275"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9372000098228455,"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":0.9372000098228455,"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":0.005499999970197678,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.004800000227987766,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6818000078201294},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6517000198364258},{"id":"https://openalex.org/keywords/common-emitter","display_name":"Common emitter","score":0.597100019454956},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5734000205993652},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5680000185966492},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5303999781608582},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5268999934196472},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5005000233650208}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8075000047683716},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6818000078201294},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6517000198364258},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6470999717712402},{"id":"https://openalex.org/C46918542","wikidata":"https://www.wikidata.org/wiki/Q1648344","display_name":"Common emitter","level":2,"score":0.597100019454956},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5734000205993652},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5680000185966492},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5303999781608582},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5268999934196472},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5005000233650208},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4747999906539917},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.36570000648498535},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3601999878883362},{"id":"https://openalex.org/C157899210","wikidata":"https://www.wikidata.org/wiki/Q1395022","display_name":"Convolutional code","level":3,"score":0.319599986076355},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.3093999922275543},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.2856999933719635},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2549000084400177}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3669033","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3669033","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:bdae7961d76c47b9b72e970badd19bfb","is_oa":true,"landing_page_url":"https://doaj.org/article/bdae7961d76c47b9b72e970badd19bfb","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 35266-35275 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3669033","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3669033","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.495243102312088}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Communication":[0],"specific":[1,73],"emitter":[2,39,74],"identification":[3,55,92,142],"(CSEI)":[4],"exploits":[5],"the":[6,86,137],"unintentional":[7],"modulation":[8],"features":[9],"embedded":[10],"in":[11,31,146],"an":[12],"emitter\u2019s":[13],"transmitted":[14],"signals,":[15],"known":[16],"as":[17],"radio":[18],"frequency":[19],"fingerprints":[20],"(RFFs),":[21],"to":[22,84,134],"distinguish":[23],"individual":[24],"emitters.":[25],"CSEI":[26],"has":[27],"been":[28],"widely":[29],"applied":[30],"both":[32],"military":[33],"and":[34,49,81,94,105,119,144],"civilian":[35],"domains":[36],"for":[37,71,108,125],"reliable":[38],"identification.":[40,75],"However,":[41],"traditional":[42],"approaches":[43],"often":[44],"depend":[45],"on":[46],"prior":[47],"information":[48],"handcrafted":[50],"features,":[51],"which":[52],"restrict":[53],"their":[54],"performance.":[56],"To":[57],"overcome":[58],"these":[59],"limitations,":[60],"this":[61],"paper":[62],"proposes":[63],"a":[64,113],"novel":[65],"deep":[66],"convolutional":[67],"feature":[68,110],"matching":[69],"framework":[70],"communication":[72],"The":[76],"method":[77],"leverages":[78],"data":[79],"augmentation":[80],"preprocessing":[82],"strategies":[83],"enrich":[85],"training":[87],"dataset,":[88],"thereby":[89],"enhancing":[90],"model":[91,127],"accuracy":[93,143],"robustness.":[95],"A":[96],"multi-dimensional":[97],"convolution":[98],"architecture":[99],"is":[100,123],"designed,":[101],"integrating":[102],"two-dimensional,":[103],"one-dimensional,":[104],"dilated":[106],"convolutions":[107],"hierarchical":[109],"extraction.":[111],"Moreover,":[112],"joint":[114],"loss":[115,122],"function":[116],"combining":[117],"cross-entropy":[118],"matrix":[120],"correlation":[121],"introduced":[124],"effective":[126],"optimization.":[128],"Experimental":[129],"evaluations":[130],"demonstrate":[131],"that,":[132],"compared":[133],"state-of-the-art":[135],"methods,":[136],"proposed":[138],"approach":[139],"achieves":[140],"superior":[141],"robustness":[145],"complex":[147],"electromagnetic":[148],"environments.":[149]},"counts_by_year":[],"updated_date":"2026-03-12T06:13:28.667946","created_date":"2026-02-28T00:00:00"}