{"id":"https://openalex.org/W2892154397","doi":"https://doi.org/10.1109/tvt.2018.2868698","title":"Automatic Modulation Classification: A Deep Learning Enabled Approach","display_name":"Automatic Modulation Classification: A Deep Learning Enabled Approach","publication_year":2018,"publication_date":"2018-09-04","ids":{"openalex":"https://openalex.org/W2892154397","doi":"https://doi.org/10.1109/tvt.2018.2868698","mag":"2892154397"},"language":"en","primary_location":{"id":"doi:10.1109/tvt.2018.2868698","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2018.2868698","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"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 Vehicular Technology","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/A5065732198","display_name":"Fan Meng","orcid":"https://orcid.org/0000-0002-9769-0057"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fan Meng","raw_affiliation_strings":["School of Information Science and Engineering, Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100676478","display_name":"Peng Chen","orcid":"https://orcid.org/0000-0002-7120-1577"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Chen","raw_affiliation_strings":["State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100943174","display_name":"Lenan Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lenan Wu","raw_affiliation_strings":["School of Information Science and Engineering, Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100733413","display_name":"Xianbin Wang","orcid":"https://orcid.org/0000-0003-4890-0748"},"institutions":[{"id":"https://openalex.org/I125749732","display_name":"Western University","ror":"https://ror.org/02grkyz14","country_code":"CA","type":"education","lineage":["https://openalex.org/I125749732"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Xianbin Wang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Western University, London, ON, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Western University, London, ON, Canada","institution_ids":["https://openalex.org/I125749732"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5065732198"],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":26.7398,"has_fulltext":false,"cited_by_count":401,"citation_normalized_percentile":{"value":0.99588092,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"67","issue":"11","first_page":"10760","last_page":"10772"},"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"}},{"id":"https://openalex.org/T10891","display_name":"Radar Systems and Signal Processing","score":0.9542999863624573,"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/computer-science","display_name":"Computer science","score":0.6958485245704651},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6816298961639404},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6361948251724243},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6015679240226746},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5921781659126282},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4978199005126953},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.48461756110191345},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47833821177482605},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46800604462623596},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4350741505622864},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.427642285823822},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4035356342792511}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6958485245704651},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6816298961639404},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6361948251724243},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6015679240226746},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5921781659126282},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4978199005126953},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.48461756110191345},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47833821177482605},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46800604462623596},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4350741505622864},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.427642285823822},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4035356342792511},{"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/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/tvt.2018.2868698","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2018.2868698","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"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 Vehicular Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3910221684","display_name":null,"funder_award_id":"61601281","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4012755444","display_name":null,"funder_award_id":"61271204","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G535560505","display_name":null,"funder_award_id":"SBK2018042259","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"},{"id":"https://openalex.org/G5546343215","display_name":null,"funder_award_id":"61471117","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8638595564","display_name":null,"funder_award_id":"61801112","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"},{"id":"https://openalex.org/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W353957520","https://openalex.org/W1522301498","https://openalex.org/W1542886316","https://openalex.org/W1580469463","https://openalex.org/W1663973292","https://openalex.org/W1723619723","https://openalex.org/W1968324238","https://openalex.org/W2003716933","https://openalex.org/W2005956500","https://openalex.org/W2032783947","https://openalex.org/W2042272696","https://openalex.org/W2048654658","https://openalex.org/W2072550346","https://openalex.org/W2091005538","https://openalex.org/W2092904426","https://openalex.org/W2112796928","https://openalex.org/W2118619036","https://openalex.org/W2120019163","https://openalex.org/W2147576921","https://openalex.org/W2147893740","https://openalex.org/W2151689215","https://openalex.org/W2156387975","https://openalex.org/W2163785141","https://openalex.org/W2165698076","https://openalex.org/W2170778725","https://openalex.org/W2272847350","https://openalex.org/W2283470457","https://openalex.org/W2289252105","https://openalex.org/W2609179406","https://openalex.org/W2734408173","https://openalex.org/W2737190155","https://openalex.org/W2741230443","https://openalex.org/W2742260513","https://openalex.org/W2745723384","https://openalex.org/W2756575923","https://openalex.org/W2764155154","https://openalex.org/W2772526503","https://openalex.org/W2808769300","https://openalex.org/W2884175398","https://openalex.org/W2919115771","https://openalex.org/W2964121744","https://openalex.org/W3104028856","https://openalex.org/W3147141794","https://openalex.org/W6631190155","https://openalex.org/W6634686403","https://openalex.org/W6658546925","https://openalex.org/W6682889407","https://openalex.org/W6745580566","https://openalex.org/W6764147061"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W2055243143","https://openalex.org/W3183901164","https://openalex.org/W3135818718","https://openalex.org/W4290188444","https://openalex.org/W3167935049","https://openalex.org/W3003905048","https://openalex.org/W2253429366","https://openalex.org/W3127975138","https://openalex.org/W2979740303"],"abstract_inverted_index":{"Automatic":[0],"modulation":[1,138],"classification":[2],"(AMC),":[3],"which":[4,72],"plays":[5],"critical":[6],"roles":[7],"in":[8,16,143],"both":[9],"civilian":[10],"and":[11,35,52,113,123,146,159,181],"military":[12],"applications,":[13],"is":[14,44,70,94,107,121,127,189],"investigated":[15],"this":[17],"paper":[18],"through":[19],"a":[20,91,117,161],"deep":[21],"learning":[22,126],"approach.":[23],"Conventional":[24],"AMCs":[25],"can":[26,154],"be":[27],"categorized":[28],"into":[29],"maximum":[30],"likelihood":[31],"(ML)":[32],"based":[33,67],"(ML-AMC)":[34],"feature-based":[36,157],"AMC.":[37],"However,":[38],"the":[39,53,77,84,98,110,124,132,147,152,156,165,171,186,197],"practical":[40],"deployment":[41],"of":[42,105,134],"ML-AMCs":[43],"difficult":[45],"due":[46],"to":[47,96,130,164,174,192],"its":[48],"high":[49],"computational":[50],"complexity,":[51],"manually":[54],"extracted":[55],"features":[56,75],"require":[57],"expert":[58],"knowledge.":[59],"Therefore,":[60],"an":[61],"end-to-end":[62],"convolution":[63],"neural":[64],"network":[65],"(CNN)":[66],"AMC":[68],"(CNN-AMC)":[69],"proposed,":[71,122],"automatically":[73],"extracts":[74],"from":[76],"long":[78],"symbol-rate":[79],"observation":[80],"sequence":[81],"along":[82],"with":[83,109],"estimated":[85],"signal-to-noise":[86],"ratio":[87],"(SNR).":[88],"With":[89,183],"CNNAMC,":[90],"unit":[92],"classifier":[93],"adopted":[95],"accommodate":[97],"varying":[99],"input":[100],"dimensions.":[101],"The":[102],"direct":[103],"training":[104,120],"CNN-AMC":[106,153],"challenging":[108],"complicated":[111],"model":[112],"complex":[114],"tasks,":[115],"so":[116],"novel":[118],"two-step":[119],"transfer":[125],"also":[128],"introduced":[129],"improve":[131],"efficiency":[133],"retraining.":[135],"Different":[136],"digital":[137],"schemes":[139],"have":[140,170],"been":[141],"considered":[142],"distinct":[144],"scenarios,":[145],"simulation":[148],"results":[149],"show":[150],"that":[151],"outperform":[155],"method,":[158],"obtain":[160],"closer":[162],"approximation":[163],"optimal":[166],"ML-AMC.":[167],"Besides,":[168],"CNN-AMCs":[169],"certain":[172],"robustness":[173],"estimation":[175],"error":[176],"on":[177],"carrier":[178],"phase":[179],"offset":[180],"SNR.":[182],"parallel":[184],"computation,":[185],"deep-learning-based":[187],"approach":[188],"about":[190],"40":[191],"1700":[193],"times":[194],"faster":[195],"than":[196],"ML-AMC":[198],"regarding":[199],"inference":[200],"speed.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":9},{"year":2025,"cited_by_count":62},{"year":2024,"cited_by_count":61},{"year":2023,"cited_by_count":59},{"year":2022,"cited_by_count":52},{"year":2021,"cited_by_count":71},{"year":2020,"cited_by_count":67},{"year":2019,"cited_by_count":18},{"year":2018,"cited_by_count":2}],"updated_date":"2026-04-11T08:14:18.477133","created_date":"2025-10-10T00:00:00"}