{"id":"https://openalex.org/W3153332423","doi":"https://doi.org/10.1109/access.2021.3073845","title":"Training Images Generation for CNN Based Automatic Modulation Classification","display_name":"Training Images Generation for CNN Based Automatic Modulation Classification","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3153332423","doi":"https://doi.org/10.1109/access.2021.3073845","mag":"3153332423"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3073845","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3073845","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09405990.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":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09405990.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100727406","display_name":"Weitao Zhang","orcid":"https://orcid.org/0000-0003-3418-6512"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei-Tao Zhang","raw_affiliation_strings":["Research Institute of Advanced Remote Sensing Technology, Xidian University, Xi\u2019an, China","School of Electronic Engineering, Xidian University, Xi\u2019an, China","School of Electronic Engineering, Xidian University, Xi'an, China","Research Institute of Advanced Remote Sensing Technology, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-3418-6512","affiliations":[{"raw_affiliation_string":"Research Institute of Advanced Remote Sensing Technology, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Research Institute of Advanced Remote Sensing Technology, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072962561","display_name":"Dan Cui","orcid":"https://orcid.org/0000-0003-4280-6697"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dan Cui","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi\u2019an, China","School of Electronic Engineering, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-4280-6697","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076579142","display_name":"Shun-Tian Lou","orcid":"https://orcid.org/0009-0006-1483-8842"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shun-Tian Lou","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi\u2019an, China","School of Electronic Engineering, Xidian University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.5932,"has_fulltext":true,"cited_by_count":27,"citation_normalized_percentile":{"value":0.91047897,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"9","issue":null,"first_page":"62916","last_page":"62925"},"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":0.953000009059906,"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"}},{"id":"https://openalex.org/T12946","display_name":"Fractal and DNA sequence analysis","score":0.9430000185966492,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"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":0.860137939453125},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8197183609008789},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7818902730941772},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7066945433616638},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.552407443523407},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5112934112548828},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5023941993713379},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.497786283493042},{"id":"https://openalex.org/keywords/baseband","display_name":"Baseband","score":0.4702383875846863},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43798547983169556},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3908413350582123},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3643648624420166},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.21660467982292175},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.17320799827575684}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.860137939453125},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8197183609008789},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7818902730941772},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7066945433616638},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.552407443523407},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5112934112548828},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5023941993713379},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.497786283493042},{"id":"https://openalex.org/C65165936","wikidata":"https://www.wikidata.org/wiki/Q575784","display_name":"Baseband","level":3,"score":0.4702383875846863},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43798547983169556},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3908413350582123},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3643648624420166},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21660467982292175},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.17320799827575684},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2021.3073845","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3073845","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09405990.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:doaj.org/article:b804879745b84be2baedfd4954d4ddcc","is_oa":true,"landing_page_url":"https://doaj.org/article/b804879745b84be2baedfd4954d4ddcc","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"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 62916-62925 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3073845","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3073845","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09405990.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":[{"id":"https://openalex.org/G4453049681","display_name":"\u5f20\u91cf\u5bf9\u89d2\u5316\u53ca\u5176\u5728\u591a\u7ef4\u6570\u636e\u7279\u5f81\u5b66\u4e60\u4e0e\u5206\u7c7b\u4e2d\u7684\u5e94\u7528\u7814\u7a76","funder_award_id":"62071350","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":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3153332423.pdf","grobid_xml":"https://content.openalex.org/works/W3153332423.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W2005956500","https://openalex.org/W2048654658","https://openalex.org/W2055224708","https://openalex.org/W2098432861","https://openalex.org/W2112638238","https://openalex.org/W2130014182","https://openalex.org/W2147735901","https://openalex.org/W2170778725","https://openalex.org/W2510754404","https://openalex.org/W2560036562","https://openalex.org/W2755335499","https://openalex.org/W2765742437","https://openalex.org/W2766252964","https://openalex.org/W2884089434","https://openalex.org/W2919115771","https://openalex.org/W2953143151","https://openalex.org/W3000943722","https://openalex.org/W3011724393","https://openalex.org/W3038716110","https://openalex.org/W3095439107","https://openalex.org/W3133033995","https://openalex.org/W6782325136"],"related_works":["https://openalex.org/W2952813363","https://openalex.org/W4360783045","https://openalex.org/W3128011703","https://openalex.org/W2963346891","https://openalex.org/W3176438653","https://openalex.org/W2770149305","https://openalex.org/W3167930666","https://openalex.org/W3014952856","https://openalex.org/W2005234362","https://openalex.org/W2162970382"],"abstract_inverted_index":{"Convolutional":[0],"neural":[1,90],"network":[2,72,91,113],"(CNN)":[3],"models":[4],"have":[5,118],"recently":[6],"demonstrated":[7],"impressive":[8],"classification":[9,144],"and":[10,15,48,74,109],"recognition":[11],"performance":[12],"on":[13],"image":[14,68],"video":[16],"processing":[17],"scope.":[18],"In":[19],"this":[20],"paper,":[21],"we":[22,83],"investigate":[23],"the":[24,37,52,60,64,78,94,103,112,124,127,130,134,138],"application":[25],"of":[26,42,77,106,126,143],"CNN":[27],"to":[28,50,85,122],"identifying":[29],"modulation":[30],"classes":[31],"for":[32,56,71,101],"digitally":[33],"modulated":[34,43],"signals.":[35],"First,":[36],"received":[38],"baseband":[39],"data":[40],"samples":[41],"signal":[44],"are":[45],"gathered":[46],"up":[47],"transformed":[49],"generate":[51],"constellation-like":[53],"training":[54,62,73,114],"images":[55],"convolutional":[57,66,89],"networks.":[58],"Among":[59],"resulting":[61],"images,":[63],"proposed":[65,128,135],"gray":[67],"is":[69,99],"preferred":[70],"inference":[75],"because":[76],"lower":[79],"computational":[80],"burden.":[81],"Second,":[82],"propose":[84],"use":[86],"a":[87],"multiple-scale":[88],"(MSCNN)":[92],"as":[93],"classifier.":[95],"The":[96],"skip-connection":[97],"technique":[98],"deployed":[100],"mitigating":[102],"negative":[104],"effect":[105],"vanishing":[107],"gradients":[108],"overfitting":[110],"during":[111],"process.":[115],"Numerical":[116],"simulations":[117],"been":[119],"carried":[120],"out":[121],"validate":[123],"effectiveness":[125],"scheme,":[129],"results":[131],"show":[132],"that":[133],"scheme":[136],"outperforms":[137],"traditional":[139],"algorithms":[140],"in":[141],"terms":[142],"accuracy.":[145]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}