{"id":"https://openalex.org/W4411996728","doi":"https://doi.org/10.1109/tvt.2025.3585536","title":"Robust Semantic Communication via Adversarial Training","display_name":"Robust Semantic Communication via Adversarial Training","publication_year":2025,"publication_date":"2025-07-03","ids":{"openalex":"https://openalex.org/W4411996728","doi":"https://doi.org/10.1109/tvt.2025.3585536"},"language":"en","primary_location":{"id":"doi:10.1109/tvt.2025.3585536","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2025.3585536","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Vehicular Technology","raw_type":"journal-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/A5088420352","display_name":"Kai Wei","orcid":"https://orcid.org/0000-0002-7240-7729"},"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":"Kai Wei","raw_affiliation_strings":["National Mobile Communications Research Laboratory (NCRL), Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-7240-7729","affiliations":[{"raw_affiliation_string":"National Mobile Communications Research Laboratory (NCRL), Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023822934","display_name":"Renjie Xie","orcid":"https://orcid.org/0000-0001-5619-9766"},"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":"Renjie Xie","raw_affiliation_strings":["School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China","School of Internet of Things, Nanjing University of Posts &amp; Telecommunications, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-5619-9766","affiliations":[{"raw_affiliation_string":"School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]},{"raw_affiliation_string":"School of Internet of Things, Nanjing University of Posts &amp; Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013867024","display_name":"Wei Xu","orcid":"https://orcid.org/0000-0001-9341-8382"},"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":"Wei Xu","raw_affiliation_strings":["National Mobile Communications Research Laboratory (NCRL), Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-9341-8382","affiliations":[{"raw_affiliation_string":"National Mobile Communications Research Laboratory (NCRL), Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114706300","display_name":"Zhaohua Lu","orcid":"https://orcid.org/0009-0009-1761-3544"},"institutions":[{"id":"https://openalex.org/I4210098582","display_name":"ZTE (China)","ror":"https://ror.org/00rjhhq63","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210098582"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaohua Lu","raw_affiliation_strings":["ZTE Corporation, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0009-1761-3544","affiliations":[{"raw_affiliation_string":"ZTE Corporation, Shenzhen, China","institution_ids":["https://openalex.org/I4210098582"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103063755","display_name":"Huahua Xiao","orcid":"https://orcid.org/0009-0001-5316-061X"},"institutions":[{"id":"https://openalex.org/I4210098582","display_name":"ZTE (China)","ror":"https://ror.org/00rjhhq63","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210098582"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huahua Xiao","raw_affiliation_strings":["ZTE Corporation, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0001-5316-061X","affiliations":[{"raw_affiliation_string":"ZTE Corporation, Shenzhen, China","institution_ids":["https://openalex.org/I4210098582"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.792,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.90984572,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"74","issue":"12","first_page":"19849","last_page":"19853"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9763000011444092,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9763000011444092,"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9035999774932861,"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/adversarial-system","display_name":"Adversarial system","score":0.7291168570518494},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5943719148635864},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.58329838514328},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4925266206264496}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7291168570518494},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5943719148635864},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.58329838514328},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4925266206264496},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tvt.2025.3585536","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2025.3585536","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Vehicular Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2618530766","https://openalex.org/W2946889564","https://openalex.org/W2969519626","https://openalex.org/W3036851434","https://openalex.org/W3102125291","https://openalex.org/W3107617916","https://openalex.org/W3190138134","https://openalex.org/W4206175303","https://openalex.org/W4293846201","https://openalex.org/W4317794926","https://openalex.org/W4321600745","https://openalex.org/W4365420366","https://openalex.org/W4366386370","https://openalex.org/W4385569494","https://openalex.org/W4390722043","https://openalex.org/W4391093002","https://openalex.org/W4402897188"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W4246396837","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492"],"abstract_inverted_index":{"The":[0],"increasing":[1,157],"demand":[2],"for":[3],"transmitting":[4],"massive":[5],"data":[6],"in":[7,76,81,87,134,210],"intelligent":[8],"communication":[9,28,53,101,197],"systems":[10,102,198],"has":[11,72],"garnered":[12],"much":[13],"attention":[14],"on":[15,32,166],"joint":[16],"source-channel":[17],"coding":[18],"(JSCC)":[19],"and":[20],"semantic":[21,27,52,77,100,196],"communication.":[22],"Existing":[23],"neural":[24],"network":[25],"(NN)-based":[26],"solutions":[29],"predominantly":[30],"focus":[31],"specific":[33,167],"channel":[34,42,67,89,105,121,135,153,168,186,208],"environments,":[35],"which":[36],"limits":[37],"their":[38],"adaptability":[39,63],"to":[40,60,113,127,180,191,206],"varying":[41,88,104],"conditions.":[43,68,187],"To":[44],"address":[45],"this":[46],"challenge,":[47],"we":[48],"present":[49],"a":[50,145,173,181],"robust":[51,195],"approach":[54,171],"that":[55,118,140,164,177],"leverages":[56],"adversarial":[57],"(ADV)":[58],"training":[59,71,143],"broaden":[61],"its":[62,79],"across":[64],"diverse":[65,120,207],"unseen":[66],"While":[69],"ADV":[70,109,116,142,201],"been":[73],"previously":[74],"explored":[75],"communications,":[78],"application":[80],"improving":[82],"the":[83,97,125,192],"robustness":[84,98],"of":[85,99,149,184,194,204],"JSCC":[86],"conditions":[90,106,209],"remains":[91],"underdeveloped.":[92],"Specifically,":[93],"our":[94,170],"method":[95],"enhances":[96],"under":[103],"by":[107,200],"incorporating":[108],"training,":[110,202],"subjecting":[111],"NNs":[112],"carefully":[114],"crafted":[115],"perturbations":[117],"mimic":[119],"distortions.":[122],"This":[123,188],"enables":[124],"NN":[126,158],"perform":[128],"more":[129,174],"effectively":[130,178],"when":[131,152],"encountering":[132],"changes":[133],"states.":[136],"Simulation":[137],"experiments":[138],"demonstrate":[139],"integrating":[141],"yields":[144],"substantial":[146],"performance":[147],"improvement":[148],"over":[150],"10%":[151],"states":[154],"change,":[155],"without":[156],"complexity.":[159],"Notably,":[160],"unlike":[161],"existing":[162],"methods":[163],"concentrate":[165],"models,":[169],"offers":[172],"generalized":[175],"solution":[176],"adapts":[179],"wide":[182],"range":[183],"real-world":[185,211],"study":[189],"contributes":[190],"development":[193],"empowered":[199],"capable":[203],"adapting":[205],"scenarios.":[212]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
