{"id":"https://openalex.org/W2995715529","doi":"https://doi.org/10.1109/dyspan.2019.8935823","title":"Estimating the Required Training Dataset Size for Transmitter Classification Using Deep Learning","display_name":"Estimating the Required Training Dataset Size for Transmitter Classification Using Deep Learning","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W2995715529","doi":"https://doi.org/10.1109/dyspan.2019.8935823","mag":"2995715529"},"language":"en","primary_location":{"id":"doi:10.1109/dyspan.2019.8935823","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dyspan.2019.8935823","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)","raw_type":"proceedings-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/A5040070604","display_name":"Taiwo Oyedare","orcid":"https://orcid.org/0000-0003-1141-4922"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Taiwo Oyedare","raw_affiliation_strings":["Bradley Department of Electrical & Computer Engineering, Virginia Tech, Arlington, VA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bradley Department of Electrical & Computer Engineering, Virginia Tech, Arlington, VA, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090830151","display_name":"Jung\u2010Min Park","orcid":"https://orcid.org/0000-0002-4879-8467"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jung-Min Jerry Park","raw_affiliation_strings":["Bradley Department of Electrical & Computer Engineering, Virginia Tech, Arlington, VA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bradley Department of Electrical & Computer Engineering, Virginia Tech, Arlington, VA, USA","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5040070604"],"corresponding_institution_ids":["https://openalex.org/I859038795"],"apc_list":null,"apc_paid":null,"fwci":2.468,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.91883649,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"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":0.9803000092506409,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9305999875068665,"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":0.7960567474365234},{"id":"https://openalex.org/keywords/rule-of-thumb","display_name":"Rule of thumb","score":0.7457970380783081},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7420241236686707},{"id":"https://openalex.org/keywords/transmitter","display_name":"Transmitter","score":0.6797052025794983},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6711500883102417},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.569915771484375},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5258686542510986},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5073735117912292},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46131300926208496},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34571573138237},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.24425756931304932},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.11167499423027039},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09249508380889893}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7960567474365234},{"id":"https://openalex.org/C89246107","wikidata":"https://www.wikidata.org/wiki/Q1398821","display_name":"Rule of thumb","level":2,"score":0.7457970380783081},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7420241236686707},{"id":"https://openalex.org/C47798520","wikidata":"https://www.wikidata.org/wiki/Q190157","display_name":"Transmitter","level":3,"score":0.6797052025794983},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6711500883102417},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.569915771484375},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5258686542510986},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5073735117912292},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46131300926208496},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34571573138237},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.24425756931304932},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.11167499423027039},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09249508380889893}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dyspan.2019.8935823","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dyspan.2019.8935823","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1569512666","https://openalex.org/W1576229127","https://openalex.org/W1980547439","https://openalex.org/W1980698266","https://openalex.org/W1998582365","https://openalex.org/W2000042664","https://openalex.org/W2076118331","https://openalex.org/W2092095117","https://openalex.org/W2108139215","https://openalex.org/W2132886902","https://openalex.org/W2141125852","https://openalex.org/W2175082661","https://openalex.org/W2183590093","https://openalex.org/W2201081010","https://openalex.org/W2272847350","https://openalex.org/W2325939864","https://openalex.org/W2590796488","https://openalex.org/W2591951844","https://openalex.org/W2601066903","https://openalex.org/W2775383661","https://openalex.org/W2775461895","https://openalex.org/W2785648464","https://openalex.org/W2791256362","https://openalex.org/W2791522365","https://openalex.org/W2884443195","https://openalex.org/W2889741439","https://openalex.org/W2962970834","https://openalex.org/W2969682019","https://openalex.org/W3103362336","https://openalex.org/W4285719527","https://openalex.org/W6685104986","https://openalex.org/W6700903540","https://openalex.org/W6733793881","https://openalex.org/W6735544697","https://openalex.org/W6745434554","https://openalex.org/W6746932923","https://openalex.org/W6748690524"],"related_works":["https://openalex.org/W2355663289","https://openalex.org/W2106913410","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Despite":[0],"the":[1,5,19,32,57,96,109,125,155,160,211],"recent":[2],"surge":[3],"in":[4,128,132,201,216],"application":[6],"of":[7,34,65,77,83,141],"deep":[8,78],"learning":[9],"to":[10,24,41,93,164],"wireless":[11,202],"communication":[12],"problems,":[13],"very":[14],"little":[15],"is":[16,48],"known":[17],"about":[18],"required":[20,212],"training":[21,46,97,112,180,213],"dataset":[22,98,113,147,181,214],"size":[23,99,114,148,215],"solve":[25],"difficult":[26],"problems":[27],"with":[28,183,187,197],"acceptable":[29],"accuracy,":[30],"including":[31],"problem":[33],"transmitter":[35,119,134,203,217],"classification.":[36,103,204],"Many":[37],"researchers":[38,89],"use":[39,159],"rules-of-thumb":[40,126],"find":[42],"out":[43,95],"how":[44],"much":[45],"data":[47],"needed":[49,100],"for":[50,70,101,118,178,210],"certain":[51],"classification":[52,116,120,135,139,176,218],"or":[53],"identification":[54],"tasks.":[55,136],"For":[56],"artificial":[58],"neural":[59,72,79],"network":[60],"(ANN)":[61],"research,":[62],"these":[63,81],"rules":[64,82],"thumb":[66,84],"may":[67,85],"suffice,":[68],"however,":[69],"convolutional":[71],"networks":[73],"(CNN),":[74],"a":[75,142,146,150,208],"class":[76],"networks,":[80],"not":[86],"hold,":[87],"and":[88,115,154],"are":[90],"often":[91],"left":[92],"Figure":[94],"accurate":[102],"In":[104],"this":[105],"paper,":[106],"we":[107,173,206],"investigate":[108],"correlation":[110],"between":[111],"accuracy":[117,177],"applications":[121],"by":[122],"investigating":[123],"whether":[124],"used":[127],"ANN":[129],"research":[130],"applies":[131],"CNN-based":[133,143],"We":[137,158,192],"predict":[138,175],"performance":[140],"architecture":[144],"given":[145],"using":[149,219],"power":[151],"law":[152],"model":[153],"Levenberg-Marquardt":[156],"algorithm.":[157],"chi-squared":[161],"goodness-of-fit":[162],"test":[163],"validate":[165],"our":[166,195],"predicted":[167],"model.":[168],"Our":[169],"results":[170],"show":[171],"that":[172],"can":[174],"larger":[179],"sizes":[182],"different":[184],"experimental":[185],"scenarios":[186],"at":[188],"least":[189],"97.5%":[190],"accuracy.":[191],"also":[193],"compare":[194],"scheme":[196],"similar":[198],"prior":[199],"works":[200],"Finally,":[205],"propose":[207],"rule-of-thumb":[209],"CNNs.":[220]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":2}],"updated_date":"2026-05-10T08:33:47.465468","created_date":"2025-10-10T00:00:00"}
