{"id":"https://openalex.org/W3112647738","doi":"https://doi.org/10.1109/lcomm.2020.3044755","title":"A Data Preprocessing Method for Automatic Modulation Classification Based on CNN","display_name":"A Data Preprocessing Method for Automatic Modulation Classification Based on CNN","publication_year":2020,"publication_date":"2020-12-14","ids":{"openalex":"https://openalex.org/W3112647738","doi":"https://doi.org/10.1109/lcomm.2020.3044755","mag":"3112647738"},"language":"en","primary_location":{"id":"doi:10.1109/lcomm.2020.3044755","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lcomm.2020.3044755","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/A5010132872","display_name":"Haozheng Zhang","orcid":"https://orcid.org/0009-0004-3009-5570"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haozheng Zhang","raw_affiliation_strings":["School of Information Science and Engineering, Yunnan University, Kunming, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Yunnan University, Kunming, China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004290542","display_name":"Ming Huang","orcid":"https://orcid.org/0000-0002-9517-2125"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Huang","raw_affiliation_strings":["School of Information Science and Engineering, Yunnan University, Kunming, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Yunnan University, Kunming, China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081970753","display_name":"Jingjing Yang","orcid":"https://orcid.org/0000-0002-3414-0621"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingjing Yang","raw_affiliation_strings":["School of Information Science and Engineering, Yunnan University, Kunming, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Yunnan University, Kunming, China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100650060","display_name":"Wei Sun","orcid":"https://orcid.org/0000-0002-1284-4062"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Sun","raw_affiliation_strings":["School of Information Science and Engineering, Yunnan University, Kunming, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Yunnan University, Kunming, China","institution_ids":["https://openalex.org/I189210763"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5010132872"],"corresponding_institution_ids":["https://openalex.org/I189210763"],"apc_list":null,"apc_paid":null,"fwci":4.2453,"has_fulltext":false,"cited_by_count":70,"citation_normalized_percentile":{"value":0.95204604,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"25","issue":"4","first_page":"1206","last_page":"1210"},"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8135684728622437},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8079500198364258},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.7656737565994263},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6988681554794312},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6920331716537476},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.667461633682251},{"id":"https://openalex.org/keywords/modulation","display_name":"Modulation (music)","score":0.5846281051635742},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4799123704433441},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.477426141500473},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.46328654885292053},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.4510292112827301},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4490457773208618},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4339877963066101},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.16766801476478577},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.09949463605880737},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08355581760406494}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8135684728622437},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8079500198364258},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.7656737565994263},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6988681554794312},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6920331716537476},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.667461633682251},{"id":"https://openalex.org/C123079801","wikidata":"https://www.wikidata.org/wiki/Q750240","display_name":"Modulation (music)","level":2,"score":0.5846281051635742},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4799123704433441},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.477426141500473},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.46328654885292053},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.4510292112827301},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4490457773208618},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4339877963066101},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.16766801476478577},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.09949463605880737},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08355581760406494},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lcomm.2020.3044755","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lcomm.2020.3044755","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/G7699460359","display_name":null,"funder_award_id":"61863035","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8691624164","display_name":null,"funder_award_id":"11564044","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8809255603","display_name":null,"funder_award_id":"61963037","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":22,"referenced_works":["https://openalex.org/W1972973610","https://openalex.org/W2005956500","https://openalex.org/W2015234197","https://openalex.org/W2114267371","https://openalex.org/W2147735901","https://openalex.org/W2155452631","https://openalex.org/W2208938511","https://openalex.org/W2272847350","https://openalex.org/W2294292751","https://openalex.org/W2556967412","https://openalex.org/W2741230443","https://openalex.org/W2909712920","https://openalex.org/W2917006683","https://openalex.org/W2919115771","https://openalex.org/W2949846184","https://openalex.org/W2963809753","https://openalex.org/W3011724393","https://openalex.org/W3022311971","https://openalex.org/W3104028856","https://openalex.org/W6694508510","https://openalex.org/W6729983426","https://openalex.org/W6758685805"],"related_works":["https://openalex.org/W2989490741","https://openalex.org/W3092506759","https://openalex.org/W2367545121","https://openalex.org/W4248881655","https://openalex.org/W2482165163","https://openalex.org/W3010890513","https://openalex.org/W120741642","https://openalex.org/W138569904","https://openalex.org/W2390914021","https://openalex.org/W2389417819"],"abstract_inverted_index":{"As":[0],"a":[1,37,43,84,98,105],"backbone":[2],"of":[3,17,27,92,108],"deep":[4],"learning":[5],"models,":[6],"convolutional":[7],"neural":[8],"networks":[9],"(CNNs)":[10],"are":[11],"widely":[12],"used":[13,60],"in":[14,61,83],"the":[15,25,65,75,90,93,111],"field":[16],"automatic":[18,54,120],"modulation":[19,55,121],"classification.":[20,56],"Nevertheless,":[21],"we":[22,96],"speculate":[23],"that":[24,73],"forms":[26],"signal":[28],"samples":[29],"make":[30],"them":[31],"inefficient":[32],"for":[33],"direct":[34],"use":[35],"as":[36],"CNN":[38,99],"input.":[39],"In":[40],"this":[41,62],"letter,":[42],"novel":[44],"data":[45],"preprocessing":[46],"method":[47,77],"is":[48,64,114],"proposed":[49,76],"to":[50,89,103],"markedly":[51],"improve":[52],"CNN-based":[53],"The":[57,69],"benchmark":[58],"dataset":[59],"research":[63],"well-known":[66],"RadioML2016.10a":[67],"dataset.":[68],"experimental":[70],"results":[71],"show":[72],"using":[74],"gains":[78],"approximately":[79],"10%":[80],"accuracy":[81,107],"improvement":[82],"simple":[85],"CNN.":[86],"Furthermore,":[87],"according":[88],"form":[91],"preprocessed":[94],"data,":[95],"designed":[97],"with":[100],"residual":[101],"blocks":[102],"reach":[104],"maximum":[106],"93.7%":[109],"when":[110],"signal-to-noise":[112],"ratio":[113],"14":[115],"dB,":[116],"which":[117],"outperforms":[118],"state-of-the-art":[119],"classifiers.":[122]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":18},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":8}],"updated_date":"2026-03-01T08:55:55.761014","created_date":"2025-10-10T00:00:00"}
