{"id":"https://openalex.org/W2971411179","doi":"https://doi.org/10.1109/access.2021.3053427","title":"Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification","display_name":"Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W2971411179","doi":"https://doi.org/10.1109/access.2021.3053427","mag":"2971411179"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3053427","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3053427","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09330604.pdf","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":["arxiv","crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09330604.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101398447","display_name":"Kaisheng Liao","orcid":"https://orcid.org/0000-0003-0782-5825"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Kaisheng Liao","raw_affiliation_strings":["Science and Technology on Electronic Information Control Laboratory, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Science and Technology on Electronic Information Control Laboratory, Chengdu, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043681711","display_name":"Yaodong Zhao","orcid":"https://orcid.org/0000-0001-7782-9771"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yaodong Zhao","raw_affiliation_strings":["Science and Technology on Electronic Information Control Laboratory, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Science and Technology on Electronic Information Control Laboratory, Chengdu, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100370036","display_name":"Jie Gu","orcid":"https://orcid.org/0000-0002-6558-169X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jie Gu","raw_affiliation_strings":["Science and Technology on Electronic Information Control Laboratory, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Science and Technology on Electronic Information Control Laboratory, Chengdu, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100366775","display_name":"Yaping Zhang","orcid":"https://orcid.org/0000-0002-3338-2151"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yaping Zhang","raw_affiliation_strings":["Science and Technology on Electronic Information Control Laboratory, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Science and Technology on Electronic Information Control Laboratory, Chengdu, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100667725","display_name":"Yi Zhong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yi Zhong","raw_affiliation_strings":["Science and Technology on Electronic Information Control Laboratory, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Science and Technology on Electronic Information Control Laboratory, Chengdu, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101398447"],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":8.9575,"has_fulltext":true,"cited_by_count":87,"citation_normalized_percentile":{"value":0.98187592,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"9","issue":null,"first_page":"27182","last_page":"27188"},"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/T10891","display_name":"Radar Systems and Signal Processing","score":0.9239000082015991,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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":0.8231489658355713},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.810577929019928},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5967895984649658},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5862686634063721},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5801418423652649},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5740196108818054},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.4584907591342926},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43761342763900757},{"id":"https://openalex.org/keywords/time-delay-neural-network","display_name":"Time delay neural network","score":0.4142431318759918},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3563867211341858}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8231489658355713},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.810577929019928},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5967895984649658},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5862686634063721},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5801418423652649},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5740196108818054},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.4584907591342926},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43761342763900757},{"id":"https://openalex.org/C175202392","wikidata":"https://www.wikidata.org/wiki/Q2434543","display_name":"Time delay neural network","level":3,"score":0.4142431318759918},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3563867211341858}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2021.3053427","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3053427","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09330604.pdf","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:arXiv.org:1909.03050","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1909.03050","pdf_url":"https://arxiv.org/pdf/1909.03050","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:doaj.org/article:2c2a30a05bd1430b8a28fa9e4c193801","is_oa":true,"landing_page_url":"https://doaj.org/article/2c2a30a05bd1430b8a28fa9e4c193801","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"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 9, Pp 27182-27188 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3053427","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3053427","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09330604.pdf","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2971411179.pdf","grobid_xml":"https://content.openalex.org/works/W2971411179.grobid-xml"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W1986532538","https://openalex.org/W2005956500","https://openalex.org/W2032783947","https://openalex.org/W2035328597","https://openalex.org/W2048654658","https://openalex.org/W2055224708","https://openalex.org/W2065424480","https://openalex.org/W2101840010","https://openalex.org/W2112781931","https://openalex.org/W2120019163","https://openalex.org/W2130014182","https://openalex.org/W2132820343","https://openalex.org/W2147735901","https://openalex.org/W2147893740","https://openalex.org/W2149256602","https://openalex.org/W2170778725","https://openalex.org/W2272847350","https://openalex.org/W2343899768","https://openalex.org/W2402144811","https://openalex.org/W2562146178","https://openalex.org/W2603396821","https://openalex.org/W2741230443","https://openalex.org/W2756575923","https://openalex.org/W2773170971","https://openalex.org/W2775383661","https://openalex.org/W2790452209","https://openalex.org/W2790896200","https://openalex.org/W2795250928","https://openalex.org/W2888024549","https://openalex.org/W2893903145","https://openalex.org/W2894603161","https://openalex.org/W2909712920","https://openalex.org/W2913173252","https://openalex.org/W2916238263","https://openalex.org/W2944891925","https://openalex.org/W2953384591","https://openalex.org/W2996489182","https://openalex.org/W3101503122","https://openalex.org/W3104028856","https://openalex.org/W6658546925","https://openalex.org/W6694508510","https://openalex.org/W6713134421","https://openalex.org/W6758685805","https://openalex.org/W6771982476","https://openalex.org/W6917408469"],"related_works":["https://openalex.org/W2810679507","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3008584592","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"A":[0,173],"novel":[1],"and":[2,26,53,75,102,125,131,187],"efficient":[3],"end-to-end":[4],"learning":[5],"model":[6],"for":[7,13,201],"automatic":[8,203],"modulation":[9,204],"classification":[10,106,171],"is":[11,155,195],"proposed":[12,87,139],"wireless":[14],"spectrum":[15],"monitoring":[16],"applications,":[17],"which":[18],"automatically":[19],"learns":[20],"from":[21,108],"the":[22,31,38,47,86,122,138,146,149,159,182,199],"time":[23,127],"domain":[24],"in-phase":[25],"quadrature":[27],"data":[28],"without":[29],"requiring":[30],"design":[32],"of":[33,40,49,67,71,78,148,161],"hand-crafted":[34],"expert":[35],"features.":[36],"With":[37],"intuition":[39],"convolutional":[41,57,72,164,176,184],"layers":[42],"with":[43,181],"pooling":[44],"serving":[45],"as":[46],"role":[48],"front-end":[50],"feature":[51],"distillation":[52],"dimensionality":[54],"reduction,":[55],"sequential":[56,163,175],"recurrent":[58,79,165,177],"neural":[59,73,80,166,178,185,193],"networks":[60,74],"are":[61,141],"developed":[62,196],"to":[63,95,110,115,143,157,197],"take":[64],"complementary":[65],"advantage":[66],"parallel":[68],"computing":[69],"capability":[70],"temporal":[76],"sensitivity":[77],"networks.":[81],"Experimental":[82],"results":[83],"demonstrate":[84],"that":[85],"architecture":[88,140,180],"delivers":[89],"overall":[90],"superior":[91],"performance":[92],"in":[93],"signal":[94,114],"noise":[96,116],"ratio":[97,117],"range":[98],"above":[99],"-10":[100],"dB,":[101],"achieves":[103],"significantly":[104],"improved":[105],"accuracy":[107],"80%":[109],"92.1%":[111],"at":[112],"high":[113],"range,":[118],"while":[119],"drastically":[120],"reduces":[121],"average":[123],"training":[124],"prediction":[126],"by":[128,137],"approximately":[129],"74%":[130],"67%,":[132],"respectively.":[133],"Response":[134],"patterns":[135],"learned":[136],"visualized":[142],"better":[144],"understand":[145],"physics":[147],"model.":[150],"Furthermore,":[151],"a":[152],"comparative":[153],"study":[154],"performed":[156],"investigate":[158],"impacts":[160],"various":[162],"network":[167,179,186,194],"structure":[168],"settings":[169],"on":[170],"performance.":[172],"representative":[174],"two-layer":[183,189],"subsequent":[188],"long":[190],"short-term":[191],"memory":[192],"suggest":[198],"option":[200],"fast":[202],"classification.":[205]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":21},{"year":2023,"cited_by_count":18},{"year":2022,"cited_by_count":15},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2019-09-12T00:00:00"}