{"id":"https://openalex.org/W4379619752","doi":"https://doi.org/10.1109/wocc58016.2023.10139354","title":"Automatic modulation recognition of communication signal based on wavelet transform combined with singular value and NCA-CNN","display_name":"Automatic modulation recognition of communication signal based on wavelet transform combined with singular value and NCA-CNN","publication_year":2023,"publication_date":"2023-05-05","ids":{"openalex":"https://openalex.org/W4379619752","doi":"https://doi.org/10.1109/wocc58016.2023.10139354"},"language":"en","primary_location":{"id":"doi:10.1109/wocc58016.2023.10139354","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/wocc58016.2023.10139354","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 32nd Wireless and Optical Communications Conference (WOCC)","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/A5109515459","display_name":"Yixin Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yixin Ding","raw_affiliation_strings":["Beijing Jiaotong University,Beijing,China","Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5109515459"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.1751,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.52971599,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"7","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":0.9998999834060669,"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":0.9998999834060669,"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.683689296245575},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.605473518371582},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5900022387504578},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5867786407470703},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5832608342170715},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5381643772125244},{"id":"https://openalex.org/keywords/wavelet-packet-decomposition","display_name":"Wavelet packet decomposition","score":0.5320751667022705},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.5278306603431702},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4828667938709259},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4384698271751404},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.433165967464447},{"id":"https://openalex.org/keywords/signal-to-noise-ratio","display_name":"Signal-to-noise ratio (imaging)","score":0.42530661821365356},{"id":"https://openalex.org/keywords/phase-shift-keying","display_name":"Phase-shift keying","score":0.414398193359375},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33741992712020874},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.15207749605178833},{"id":"https://openalex.org/keywords/bit-error-rate","display_name":"Bit error rate","score":0.09082597494125366},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08047884702682495}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.683689296245575},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.605473518371582},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5900022387504578},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5867786407470703},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5832608342170715},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5381643772125244},{"id":"https://openalex.org/C155777637","wikidata":"https://www.wikidata.org/wiki/Q2736187","display_name":"Wavelet packet decomposition","level":4,"score":0.5320751667022705},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.5278306603431702},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4828667938709259},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4384698271751404},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.433165967464447},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.42530661821365356},{"id":"https://openalex.org/C186378180","wikidata":"https://www.wikidata.org/wiki/Q4874866","display_name":"Phase-shift keying","level":4,"score":0.414398193359375},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33741992712020874},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.15207749605178833},{"id":"https://openalex.org/C56296756","wikidata":"https://www.wikidata.org/wiki/Q840922","display_name":"Bit error rate","level":3,"score":0.09082597494125366},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08047884702682495},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wocc58016.2023.10139354","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/wocc58016.2023.10139354","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 32nd Wireless and Optical Communications Conference (WOCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8299999833106995}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2092904426","https://openalex.org/W2565645249","https://openalex.org/W2884089434","https://openalex.org/W2943417237","https://openalex.org/W3037686265","https://openalex.org/W3101503122","https://openalex.org/W4226124747","https://openalex.org/W4231773898","https://openalex.org/W4293106263","https://openalex.org/W4308527748","https://openalex.org/W4310347449","https://openalex.org/W6779546908"],"related_works":["https://openalex.org/W2054017055","https://openalex.org/W2085792030","https://openalex.org/W2034318424","https://openalex.org/W1588899229","https://openalex.org/W4321517526","https://openalex.org/W1976022598","https://openalex.org/W1967182499","https://openalex.org/W2111896212","https://openalex.org/W2097034666","https://openalex.org/W2152748622"],"abstract_inverted_index":{"In":[0,109],"communication":[1,24],"signal":[2,129],"recognition,":[3],"there":[4],"are":[5],"problems":[6],"such":[7],"as":[8,62],"a":[9],"tedious":[10],"feature":[11],"extraction":[12],"process":[13],"and":[14,26,56,79,117,125,131],"low":[15],"applicability":[16],"of":[17,69,119,128,135,145],"extracted":[18],"features.":[19,65],"This":[20],"paper":[21,112],"simulates":[22],"wireless":[23],"channels":[25],"suggests":[27],"an":[28],"algorithm":[29,46],"that":[30],"uses":[31],"nearest":[32],"neighbor":[33],"component":[34],"analysis":[35],"(NCA)":[36],"along":[37],"with":[38],"convolutional":[39],"neural":[40],"networks":[41],"(CNN)":[42],"for":[43,97],"classification.":[44],"The":[45],"chooses":[47],"wavelet":[48,51],"entropy":[49],"(WE),":[50],"approximate":[52],"energy":[53],"ratio":[54,106],"(WAER),":[55],"the":[57,63,86,90,93,98,104,115,120,133,136,143,146],"first":[58],"2\u20134":[59],"singular":[60],"values":[61],"core":[64],"Eight":[66],"different":[67,139],"forms":[68],"modulations,":[70],"including":[71],"GFSK,":[72],"CPFSK,":[73],"B-FM,":[74],"DSB-AM,":[75],"SSB-AM,":[76],"BPSK,":[77],"QPSK":[78],"PAM4":[80],"would":[81],"be":[82],"automatically":[83],"classified":[84],"using":[85],"technique.":[87],"According":[88],"to":[89,122],"experiment":[91],"results,":[92],"average":[94],"recognition":[95,137],"accuracy":[96,118,134],"eight":[99],"signals":[100],"is":[101,107],"93.6%":[102],"when":[103],"signal-to-noise":[105,140],"30dB.":[108],"addition,":[110],"this":[111],"also":[113],"discusses":[114],"results":[116],"model":[121],"identify":[123],"6":[124],"10":[126],"types":[127],"modulation":[130],"studies":[132],"under":[138],"ratios,":[141],"verifying":[142],"robustness":[144],"model.":[147]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}
