{"id":"https://openalex.org/W2889017409","doi":"https://doi.org/10.1109/spawc.2018.8445938","title":"A Deep Learning Approach for Modulation Recognition via Exploiting Temporal Correlations","display_name":"A Deep Learning Approach for Modulation Recognition via Exploiting Temporal Correlations","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2889017409","doi":"https://doi.org/10.1109/spawc.2018.8445938","mag":"2889017409"},"language":"en","primary_location":{"id":"doi:10.1109/spawc.2018.8445938","is_oa":false,"landing_page_url":"https://doi.org/10.1109/spawc.2018.8445938","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","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/A5025749141","display_name":"Yanlun Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanlun Wu","raw_affiliation_strings":["National Key Laboratory of Science and Technology on Communications, University of Electronic Sience and Technologgy of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Science and Technology on Communications, University of Electronic Sience and Technologgy of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100770665","display_name":"Xingjian Li","orcid":"https://orcid.org/0000-0002-0668-7080"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingjian Li","raw_affiliation_strings":["National Key Laboratory of Science and Technology on Communications, University of Electronic Sience and Technologgy of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Science and Technology on Communications, University of Electronic Sience and Technologgy of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067626381","display_name":"Jun Fang","orcid":"https://orcid.org/0000-0001-7427-4723"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Fang","raw_affiliation_strings":["National Key Laboratory of Science and Technology on Communications, University of Electronic Sience and Technologgy of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Science and Technology on Communications, University of Electronic Sience and Technologgy of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":57,"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":0.9998999834060669,"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"}},{"id":"https://openalex.org/T12447","display_name":"Spider Taxonomy and Behavior Studies","score":0.9082000255584717,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7848639488220215},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.726071834564209},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.721670389175415},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6903476715087891},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.643160343170166},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.637010931968689},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4869585931301117},{"id":"https://openalex.org/keywords/modulation","display_name":"Modulation (music)","score":0.4840185344219208},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.47575873136520386},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4496423006057739},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3725910484790802}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7848639488220215},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.726071834564209},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.721670389175415},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6903476715087891},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.643160343170166},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.637010931968689},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4869585931301117},{"id":"https://openalex.org/C123079801","wikidata":"https://www.wikidata.org/wiki/Q750240","display_name":"Modulation (music)","level":2,"score":0.4840185344219208},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.47575873136520386},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4496423006057739},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3725910484790802},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C107038049","wikidata":"https://www.wikidata.org/wiki/Q35986","display_name":"Aesthetics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/spawc.2018.8445938","is_oa":false,"landing_page_url":"https://doi.org/10.1109/spawc.2018.8445938","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1991754220","https://openalex.org/W1994143366","https://openalex.org/W2004294123","https://openalex.org/W2010177220","https://openalex.org/W2019162769","https://openalex.org/W2042272696","https://openalex.org/W2048654658","https://openalex.org/W2064675550","https://openalex.org/W2095705004","https://openalex.org/W2122859478","https://openalex.org/W2130014182","https://openalex.org/W2134457835","https://openalex.org/W2147735901","https://openalex.org/W2157331557","https://openalex.org/W2166911748","https://openalex.org/W2271840356","https://openalex.org/W2272847350","https://openalex.org/W2293078015","https://openalex.org/W2919115771","https://openalex.org/W2964121744","https://openalex.org/W6674330103","https://openalex.org/W6680074393"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4239306820","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"We":[0],"consider":[1],"the":[2,23,27,31,47,51,67,86,95,119,126,144,148],"problem":[3],"of":[4,26,33,50,56,69,107,147],"automatic":[5,59],"modulation":[6,13,24,48,60,89],"recognition":[7,61],"for":[8,88],"either":[9],"digital":[10],"or":[11],"analogue":[12],"types.":[14],"The":[15,54],"receiver":[16],"does":[17],"not":[18],"have":[19],"any":[20],"knowledge":[21],"about":[22],"type":[25,49],"received":[28,52],"signal,":[29],"and":[30,72,97],"objective":[32],"this":[34,75],"paper":[35],"is":[36,63],"to":[37,79,84,92],"develop":[38],"a":[39,80,102,108,114,131,136],"deep":[40,81,103],"learning":[41,82],"approach":[42,83],"that":[43,125,141],"can":[44],"automatically":[45],"recognize":[46],"signal.":[53,149],"performance":[55],"most":[57],"existing":[58],"algorithms":[62],"highly":[64],"dependent":[65],"on":[66],"choice":[68],"key":[70],"features":[71],"classifiers.":[73],"In":[74],"paper,":[76],"we":[77,100],"resort":[78],"improve":[85],"robustness":[87],"recognition.":[90],"Specifically,":[91],"efficiently":[93],"explore":[94],"temporal":[96],"spatial":[98,145],"correlation,":[99],"construct":[101],"neural":[104,110,138],"network":[105,111,128,139],"consisting":[106],"convolutional":[109,137],"followed":[112],"by":[113],"long":[115],"short-term":[116],"memory":[117],"as":[118],"classifier.":[120],"Our":[121],"experimental":[122],"results":[123],"show":[124],"proposed":[127],"architecture":[129,140],"achieves":[130],"higher":[132],"classification":[133],"accuracy":[134],"than":[135],"exploits":[142],"only":[143],"correlation":[146]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":15},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
