{"id":"https://openalex.org/W7128443858","doi":"https://doi.org/10.1109/tccn.2026.3662331","title":"Synergistic Dual-Stream Neural Network Based on Adaptive Variational Mode Decomposition Denoising for Automatic Modulation Classification","display_name":"Synergistic Dual-Stream Neural Network Based on Adaptive Variational Mode Decomposition Denoising for Automatic Modulation Classification","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7128443858","doi":"https://doi.org/10.1109/tccn.2026.3662331"},"language":null,"primary_location":{"id":"doi:10.1109/tccn.2026.3662331","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tccn.2026.3662331","pdf_url":null,"source":{"id":"https://openalex.org/S2484188435","display_name":"IEEE Transactions on Cognitive Communications and Networking","issn_l":"2332-7731","issn":["2332-7731","2372-2045"],"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 Cognitive Communications and Networking","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":null,"display_name":"Yurui Zheng","orcid":"https://orcid.org/0009-0005-4767-658X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yurui Zheng","raw_affiliation_strings":["Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0005-4767-658X","affiliations":[{"raw_affiliation_string":"Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124421230","display_name":"Sai Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sai Huang","raw_affiliation_strings":["Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-5186-3362","affiliations":[{"raw_affiliation_string":"Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115596141","display_name":"Pengcheng Zhang","orcid":"https://orcid.org/0009-0002-6198-7999"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengcheng Zhang","raw_affiliation_strings":["Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100419861","display_name":"Yong Zhang","orcid":"https://orcid.org/0000-0003-2179-3698"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifan Zhang","raw_affiliation_strings":["Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5125455871","display_name":"Zhiyong Feng","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyong Feng","raw_affiliation_strings":["Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5322-222X","affiliations":[{"raw_affiliation_string":"Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13867015,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":null,"first_page":"6061","last_page":"6075"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9968000054359436,"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":0.9968000054359436,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T11873","display_name":"PAPR reduction in OFDM","score":0.00019999999494757503,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5112000107765198},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5051000118255615},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.4894999861717224},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4449999928474426},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4375999867916107},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.3630000054836273},{"id":"https://openalex.org/keywords/hilbert\u2013huang-transform","display_name":"Hilbert\u2013Huang transform","score":0.33320000767707825},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.32510000467300415}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8416000008583069},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5317000150680542},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5112000107765198},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5051000118255615},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.4894999861717224},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4449999928474426},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4375999867916107},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.3630000054836273},{"id":"https://openalex.org/C25570617","wikidata":"https://www.wikidata.org/wiki/Q1006462","display_name":"Hilbert\u2013Huang transform","level":3,"score":0.33320000767707825},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.32510000467300415},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.32249999046325684},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3197999894618988},{"id":"https://openalex.org/C123079801","wikidata":"https://www.wikidata.org/wiki/Q750240","display_name":"Modulation (music)","level":2,"score":0.3172999918460846},{"id":"https://openalex.org/C149946192","wikidata":"https://www.wikidata.org/wiki/Q3235733","display_name":"Cognitive radio","level":3,"score":0.305400013923645},{"id":"https://openalex.org/C138916503","wikidata":"https://www.wikidata.org/wiki/Q2714551","display_name":"Link adaptation","level":4,"score":0.29760000109672546},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2833000123500824},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2685000002384186},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2614000141620636}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tccn.2026.3662331","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tccn.2026.3662331","pdf_url":null,"source":{"id":"https://openalex.org/S2484188435","display_name":"IEEE Transactions on Cognitive Communications and Networking","issn_l":"2332-7731","issn":["2332-7731","2372-2045"],"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 Cognitive Communications and Networking","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7192150841","display_name":null,"funder_award_id":"62422103","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8129143660","display_name":null,"funder_award_id":"62321001","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W2000982976","https://openalex.org/W2005956500","https://openalex.org/W2032783947","https://openalex.org/W2057624533","https://openalex.org/W2088331655","https://openalex.org/W2092904426","https://openalex.org/W2114267371","https://openalex.org/W2130014182","https://openalex.org/W2140070574","https://openalex.org/W2147735901","https://openalex.org/W2170778725","https://openalex.org/W2194775991","https://openalex.org/W2560119283","https://openalex.org/W2612030956","https://openalex.org/W2741230443","https://openalex.org/W2803445619","https://openalex.org/W2884089434","https://openalex.org/W2916238263","https://openalex.org/W2963125010","https://openalex.org/W2963809753","https://openalex.org/W2977117446","https://openalex.org/W2983220838","https://openalex.org/W3022311971","https://openalex.org/W3026464726","https://openalex.org/W3032977069","https://openalex.org/W3034552520","https://openalex.org/W3048385670","https://openalex.org/W3085631338","https://openalex.org/W3101503122","https://openalex.org/W3120291272","https://openalex.org/W3126877825","https://openalex.org/W3193052805","https://openalex.org/W4312450940","https://openalex.org/W4312480037","https://openalex.org/W4312960790","https://openalex.org/W4323338327","https://openalex.org/W4324290967","https://openalex.org/W4375869042","https://openalex.org/W4385245566","https://openalex.org/W4385453438","https://openalex.org/W4386432075","https://openalex.org/W4388269918","https://openalex.org/W4394862802","https://openalex.org/W4400762160","https://openalex.org/W4404057454","https://openalex.org/W4408092214"],"related_works":[],"abstract_inverted_index":{"Automatic":[0],"Modulation":[1],"Classification":[2],"(AMC)":[3],"is":[4,34,202],"a":[5,14,115,121,146,160,166,197,240],"key":[6,136],"technology":[7],"for":[8,74,254],"electromagnetic":[9],"signal":[10,137],"cognition":[11],"and":[12,21,44,94,120,153,165,175,186],"plays":[13],"pivotal":[15],"role":[16],"in":[17,40,245,256],"intelligent":[18],"spectrum":[19],"sensing":[20],"management.":[22],"Although":[23],"deep":[24],"learning-based":[25],"AMC":[26],"has":[27],"become":[28],"the":[29,45,81,90,157,179],"mainstream":[30],"paradigm,":[31],"its":[32,252],"potential":[33],"constrained":[35],"by":[36,233],"insufficient":[37],"internal":[38],"synergy":[39],"hybrid":[41,83],"network":[42,72,79,84,101],"architectures":[43],"degradation":[46],"of":[47,92,99],"feature":[48,102,117],"extraction":[49,103,118,163],"capability":[50],"at":[51],"low":[52,229],"signal-to-noise":[53],"ratios":[54],"(SNRs).":[55],"To":[56],"address":[57],"these":[58],"limitations,":[59],"this":[60],"paper":[61],"proposes":[62],"an":[63,110,128],"adaptive":[64,111,129],"variational":[65],"mode":[66],"decomposition":[67],"(VMD)":[68],"denoising-based":[69],"synergistic":[70,97,147],"dual-stream":[71,116,148],"(AVDSDN)":[73],"modulation":[75],"classification.":[76],"The":[77],"proposed":[78],"decouples":[80],"traditional":[82],"structure,":[85],"enabling":[86],"it":[87],"to":[88,171,190,204,222,242],"mitigate":[89],"impact":[91],"noise":[93,141],"achieve":[95],"full":[96],"utilization":[98],"different":[100],"capabilities.":[104],"AVDSDN":[105,217,238],"comprises":[106],"three":[107,212],"core":[108],"components:":[109],"VMD":[112,130],"denoising":[113,131],"module,":[114,119],"fusion":[122,200],"module.":[123],"Specifically,":[124],"we":[125,144],"first":[126],"propose":[127],"mechanism":[132],"that":[133,216],"selectively":[134],"enhances":[135],"components":[138],"while":[139,178,247],"suppressing":[140],"components.":[142],"Subsequently,":[143],"develop":[145],"module":[149,201],"comprising":[150],"parallel":[151],"CNN":[152,158],"Transformer":[154,180],"streams.":[155,209],"In":[156],"stream,":[159],"multi-feature":[161],"interaction":[162],"block":[164],"lightweight":[167],"backbone":[168],"are":[169],"designed":[170,203],"capture":[172],"robust":[173],"local":[174],"spatial":[176],"features,":[177],"stream":[181],"innovatively":[182],"integrates":[183],"deformable":[184],"attention":[185,199],"convolutional":[187],"feed-forward":[188],"networks":[189],"efficiently":[191],"model":[192],"long-range":[193],"temporal":[194],"dependencies.":[195],"Finally,":[196],"cross-stream":[198],"synthesize":[205],"features":[206],"from":[207],"both":[208],"Simulations":[210],"on":[211],"public":[213],"datasets":[214],"demonstrate":[215],"achieves":[218,239],"superior":[219],"performance":[220],"compared":[221],"current":[223],"state-of-the-art":[224],"(SOTA)":[225],"algorithms,":[226],"especially":[227],"under":[228],"SNR":[230],"conditions.":[231],"Furthermore,":[232],"employing":[234],"post-training":[235],"quantization":[236],"(PTQ),":[237],"5-":[241],"9-fold":[243],"speedup":[244],"inference":[246],"maintaining":[248],"high":[249],"accuracy,":[250],"demonstrating":[251],"suitability":[253],"deployment":[255],"real-time":[257],"systems.":[258]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2026-02-10T00:00:00"}