{"id":"https://openalex.org/W3193052805","doi":"https://doi.org/10.1109/lcomm.2021.3102656","title":"An Efficient Deep Learning Model for Automatic Modulation Recognition Based on Parameter Estimation and Transformation","display_name":"An Efficient Deep Learning Model for Automatic Modulation Recognition Based on Parameter Estimation and Transformation","publication_year":2021,"publication_date":"2021-08-05","ids":{"openalex":"https://openalex.org/W3193052805","doi":"https://doi.org/10.1109/lcomm.2021.3102656","mag":"3193052805"},"language":"en","primary_location":{"id":"doi:10.1109/lcomm.2021.3102656","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lcomm.2021.3102656","pdf_url":null,"source":{"id":"https://openalex.org/S147316732","display_name":"IEEE Communications Letters","issn_l":"1089-7798","issn":["1089-7798","1558-2558","2373-7891"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316002","host_organization_name":"IEEE Communications Society","host_organization_lineage":["https://openalex.org/P4310316002","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Communications Society","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 Communications Letters","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/A5101657449","display_name":"Fuxin Zhang","orcid":"https://orcid.org/0000-0003-0842-2550"},"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":"Fuxin Zhang","raw_affiliation_strings":["School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0003-0842-2550","affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038985671","display_name":"Chunbo Luo","orcid":"https://orcid.org/0000-0002-9860-2901"},"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"]},{"id":"https://openalex.org/I3018263800","display_name":"Huzhou University","ror":"https://ror.org/04mvpxy20","country_code":"CN","type":"education","lineage":["https://openalex.org/I3018263800"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunbo Luo","raw_affiliation_strings":["School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China","Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-9860-2901","affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]},{"raw_affiliation_string":"Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China","institution_ids":["https://openalex.org/I3018263800"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066662339","display_name":"Jialang Xu","orcid":"https://orcid.org/0000-0003-2324-7033"},"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":"Jialang Xu","raw_affiliation_strings":["School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0003-2324-7033","affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090414684","display_name":"Yang Luo","orcid":"https://orcid.org/0000-0002-4576-5934"},"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"]},{"id":"https://openalex.org/I3018263800","display_name":"Huzhou University","ror":"https://ror.org/04mvpxy20","country_code":"CN","type":"education","lineage":["https://openalex.org/I3018263800"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Luo","raw_affiliation_strings":["School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China","Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-4576-5934","affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]},{"raw_affiliation_string":"Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China","institution_ids":["https://openalex.org/I3018263800"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":16.6492,"has_fulltext":false,"cited_by_count":231,"citation_normalized_percentile":{"value":0.99321215,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"25","issue":"10","first_page":"3287","last_page":"3290"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":1.0,"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":1.0,"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/computer-science","display_name":"Computer science","score":0.8159873485565186},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7177033424377441},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6923645734786987},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6549451351165771},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6387633085250854},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6272310018539429},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6033661961555481},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.5437269806861877},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.5080618858337402},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4807288944721222},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4495951235294342},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4422224462032318},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.21700140833854675}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8159873485565186},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7177033424377441},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6923645734786987},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6549451351165771},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6387633085250854},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6272310018539429},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6033661961555481},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.5437269806861877},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.5080618858337402},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4807288944721222},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4495951235294342},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4422224462032318},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.21700140833854675},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lcomm.2021.3102656","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lcomm.2021.3102656","pdf_url":null,"source":{"id":"https://openalex.org/S147316732","display_name":"IEEE Communications Letters","issn_l":"1089-7798","issn":["1089-7798","1558-2558","2373-7891"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316002","host_organization_name":"IEEE Communications Society","host_organization_lineage":["https://openalex.org/P4310316002","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Communications Society","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 Communications Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7588232855","display_name":"\u57fa\u4e8e\u7ec6\u7c92\u5ea6\u6ce2\u675f\u6210\u5f62\u7684\u7a7a\u57fa\u5e73\u53f0\u6beb\u7c73\u6ce2\u5168\u53cc\u5de5\u901a\u4fe1\u5173\u952e\u6280\u672f\u7814\u7a76","funder_award_id":"61871096","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2005956500","https://openalex.org/W2272847350","https://openalex.org/W2562146178","https://openalex.org/W2605344455","https://openalex.org/W2734408173","https://openalex.org/W2741230443","https://openalex.org/W2764043458","https://openalex.org/W2773170971","https://openalex.org/W2795250928","https://openalex.org/W2963730508","https://openalex.org/W3000943722","https://openalex.org/W3003174479","https://openalex.org/W3003494250","https://openalex.org/W3032977069","https://openalex.org/W3042743426","https://openalex.org/W3104028856","https://openalex.org/W3113059984","https://openalex.org/W6745148473","https://openalex.org/W6917408469"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W4246352526","https://openalex.org/W2121910908","https://openalex.org/W915438175","https://openalex.org/W4230315250"],"abstract_inverted_index":{"Automatic":[0],"modulation":[1,15],"recognition":[2,36,92,130,145],"(AMR)":[3],"is":[4,115],"a":[5,104],"promising":[6],"technology":[7],"for":[8],"intelligent":[9],"communication":[10],"receivers":[11],"to":[12,39,54,95,136],"detect":[13],"signal":[14],"schemes.":[16],"Recently,":[17],"the":[18,56,84,96,107,125,144,156],"emerging":[19],"deep":[20],"learning":[21],"(DL)":[22],"research":[23],"has":[24,151],"facilitated":[25],"high-performance":[26],"DL-AMR":[27,31,64],"approaches.":[28],"However,":[29],"most":[30],"models":[32,52,99,127],"only":[33],"focus":[34],"on":[35,67],"accuracy,":[37],"leading":[38],"huge":[40],"model":[41,65,114,139],"sizes":[42],"and":[43,50,71,78,121],"high":[44,91],"computational":[45],"complexity,":[46],"while":[47,150],"some":[48],"lightweight":[49],"low-complexity":[51],"struggle":[53],"meet":[55],"accuracy":[57,93,146],"requirements.":[58],"This":[59],"letter":[60],"proposes":[61],"an":[62],"efficient":[63],"based":[66],"phase":[68],"parameter":[69],"estimation":[70],"transformation,":[72],"with":[73,128,161],"convolutional":[74],"neural":[75],"network":[76],"(CNN)":[77],"gated":[79],"recurrent":[80],"unit":[81],"(GRU)":[82],"as":[83],"feature":[85],"extraction":[86],"layers,":[87],"which":[88,142],"can":[89],"achieve":[90],"equivalent":[94],"existing":[97],"state-of-the-art":[98,162],"but":[100],"reduces":[101],"more":[102,116],"than":[103,124,148,153],"third":[105],"of":[106,109,155,158],"volume":[108],"their":[110],"parameters.":[111],"Meanwhile,":[112],"our":[113,138],"competitive":[117],"in":[118],"training":[119],"time":[120,123],"test":[122],"benchmark":[126],"similar":[129],"accuracy.":[131],"Moreover,":[132],"we":[133],"further":[134],"propose":[135],"compress":[137],"by":[140],"pruning,":[141],"maintains":[143],"higher":[147],"90%":[149],"less":[152],"1/8":[154],"number":[157],"parameters":[159],"comparing":[160],"models.":[163]},"counts_by_year":[{"year":2026,"cited_by_count":23},{"year":2025,"cited_by_count":89},{"year":2024,"cited_by_count":72},{"year":2023,"cited_by_count":34},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-19T17:40:00.097472","created_date":"2025-10-10T00:00:00"}
