{"id":"https://openalex.org/W4399304025","doi":"https://doi.org/10.3390/make6020057","title":"An Analysis of Radio Frequency Transfer Learning Behavior","display_name":"An Analysis of Radio Frequency Transfer Learning Behavior","publication_year":2024,"publication_date":"2024-06-03","ids":{"openalex":"https://openalex.org/W4399304025","doi":"https://doi.org/10.3390/make6020057"},"language":"en","primary_location":{"id":"doi:10.3390/make6020057","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make6020057","pdf_url":"https://www.mdpi.com/2504-4990/6/2/57/pdf?version=1717414270","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/6/2/57/pdf?version=1717414270","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045887475","display_name":"Lauren J. Wong","orcid":"https://orcid.org/0000-0001-5896-0176"},"institutions":[{"id":"https://openalex.org/I1343180700","display_name":"Intel (United States)","ror":"https://ror.org/01ek73717","country_code":"US","type":"company","lineage":["https://openalex.org/I1343180700"]},{"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":"Lauren J. Wong","raw_affiliation_strings":["Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24060, USA","Intel AI Lab, Santa Clara, CA 95054, USA","National Security Institute, Virginia Tech, Blacksburg, VA 24060, USA"],"raw_orcid":"https://orcid.org/0000-0001-5896-0176","affiliations":[{"raw_affiliation_string":"Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24060, USA","institution_ids":["https://openalex.org/I859038795"]},{"raw_affiliation_string":"Intel AI Lab, Santa Clara, CA 95054, USA","institution_ids":["https://openalex.org/I1343180700"]},{"raw_affiliation_string":"National Security Institute, Virginia Tech, Blacksburg, VA 24060, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101945482","display_name":"Braeden P. Muller","orcid":"https://orcid.org/0009-0001-2040-3101"},"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":"Braeden Muller","raw_affiliation_strings":["Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24060, USA","National Security Institute, Virginia Tech, Blacksburg, VA 24060, USA"],"raw_orcid":"https://orcid.org/0009-0001-2040-3101","affiliations":[{"raw_affiliation_string":"Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24060, USA","institution_ids":["https://openalex.org/I859038795"]},{"raw_affiliation_string":"National Security Institute, Virginia Tech, Blacksburg, VA 24060, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001456630","display_name":"Sean McPherson","orcid":"https://orcid.org/0000-0002-6244-6038"},"institutions":[{"id":"https://openalex.org/I1343180700","display_name":"Intel (United States)","ror":"https://ror.org/01ek73717","country_code":"US","type":"company","lineage":["https://openalex.org/I1343180700"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sean McPherson","raw_affiliation_strings":["Intel AI Lab, Santa Clara, CA 95054, USA"],"raw_orcid":"https://orcid.org/0000-0002-6244-6038","affiliations":[{"raw_affiliation_string":"Intel AI Lab, Santa Clara, CA 95054, USA","institution_ids":["https://openalex.org/I1343180700"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011390669","display_name":"Alan J. Michaels","orcid":"https://orcid.org/0000-0003-2437-3410"},"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":"Alan J. Michaels","raw_affiliation_strings":["Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24060, USA","National Security Institute, Virginia Tech, Blacksburg, VA 24060, USA"],"raw_orcid":"https://orcid.org/0000-0003-2437-3410","affiliations":[{"raw_affiliation_string":"Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24060, USA","institution_ids":["https://openalex.org/I859038795"]},{"raw_affiliation_string":"National Security Institute, Virginia Tech, Blacksburg, VA 24060, USA","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5045887475"],"corresponding_institution_ids":["https://openalex.org/I1343180700","https://openalex.org/I859038795"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.9762,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.78873819,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"6","issue":"2","first_page":"1210","last_page":"1242"},"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"}},{"id":"https://openalex.org/T10891","display_name":"Radar Systems and Signal Processing","score":0.9668999910354614,"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9165999889373779,"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":0.714684009552002},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6701606512069702},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6684130430221558},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.6643489599227905},{"id":"https://openalex.org/keywords/radio-frequency","display_name":"Radio frequency","score":0.6473398208618164},{"id":"https://openalex.org/keywords/transmitter","display_name":"Transmitter","score":0.5176330208778381},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.46854910254478455},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4348939061164856},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4287784993648529},{"id":"https://openalex.org/keywords/radio-spectrum","display_name":"Radio spectrum","score":0.4184550642967224},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4095519185066223},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.356416255235672},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.19206121563911438},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.09260579943656921}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.714684009552002},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6701606512069702},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6684130430221558},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.6643489599227905},{"id":"https://openalex.org/C74064498","wikidata":"https://www.wikidata.org/wiki/Q3396184","display_name":"Radio frequency","level":2,"score":0.6473398208618164},{"id":"https://openalex.org/C47798520","wikidata":"https://www.wikidata.org/wiki/Q190157","display_name":"Transmitter","level":3,"score":0.5176330208778381},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.46854910254478455},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4348939061164856},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4287784993648529},{"id":"https://openalex.org/C92545706","wikidata":"https://www.wikidata.org/wiki/Q902174","display_name":"Radio spectrum","level":2,"score":0.4184550642967224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4095519185066223},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.356416255235672},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.19206121563911438},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.09260579943656921},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/make6020057","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make6020057","pdf_url":"https://www.mdpi.com/2504-4990/6/2/57/pdf?version=1717414270","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:vtechworks.lib.vt.edu:10919/119526","is_oa":true,"landing_page_url":"https://hdl.handle.net/10919/119526","pdf_url":"https://vtechworks.lib.vt.edu/bitstreams/066b887f-108f-411b-81e9-99bf34ba4782/download","source":{"id":"https://openalex.org/S4306400248","display_name":"VTechWorks (Virginia Tech)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I859038795","host_organization_name":"Virginia Tech","host_organization_lineage":["https://openalex.org/I859038795"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:43187fdd23d745d98281d08a5e5ec009","is_oa":true,"landing_page_url":"https://doaj.org/article/43187fdd23d745d98281d08a5e5ec009","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":"Machine Learning and Knowledge Extraction, Vol 6, Iss 2, Pp 1210-1242 (2024)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2504-4990/6/2/57/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/make6020057","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/make6020057","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make6020057","pdf_url":"https://www.mdpi.com/2504-4990/6/2/57/pdf?version=1717414270","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8100000023841858}],"awards":[],"funders":[{"id":"https://openalex.org/F4320333051","display_name":"Intelligence Advanced Research Projects Activity","ror":"https://ror.org/01v3fsc55"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4399304025.pdf"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W2005956500","https://openalex.org/W2165698076","https://openalex.org/W2280985394","https://openalex.org/W2603396821","https://openalex.org/W2736068844","https://openalex.org/W2740582239","https://openalex.org/W2773170971","https://openalex.org/W2798381792","https://openalex.org/W2886895257","https://openalex.org/W2889741439","https://openalex.org/W2898158860","https://openalex.org/W2903139904","https://openalex.org/W2953958347","https://openalex.org/W2954992643","https://openalex.org/W2964059111","https://openalex.org/W2964185501","https://openalex.org/W2973209754","https://openalex.org/W2973449430","https://openalex.org/W2998795925","https://openalex.org/W2999880676","https://openalex.org/W3007522628","https://openalex.org/W3010867338","https://openalex.org/W3011422503","https://openalex.org/W3015448029","https://openalex.org/W3084050033","https://openalex.org/W3092192383","https://openalex.org/W3106003309","https://openalex.org/W3121018958","https://openalex.org/W3132417220","https://openalex.org/W3137268093","https://openalex.org/W3138872598","https://openalex.org/W3174189432","https://openalex.org/W3198945532","https://openalex.org/W3201233682","https://openalex.org/W3216470025","https://openalex.org/W4212777620","https://openalex.org/W4213458747","https://openalex.org/W4295312788","https://openalex.org/W4313125090","https://openalex.org/W4321597251","https://openalex.org/W4387010052","https://openalex.org/W4387350788","https://openalex.org/W6739651123","https://openalex.org/W6755360239","https://openalex.org/W6766978945","https://openalex.org/W6780052409"],"related_works":["https://openalex.org/W4396525274","https://openalex.org/W3095487414","https://openalex.org/W2901026139","https://openalex.org/W2365220671","https://openalex.org/W2564217976","https://openalex.org/W3047331504","https://openalex.org/W3023847553","https://openalex.org/W4386211564","https://openalex.org/W2902182263","https://openalex.org/W4400985605"],"abstract_inverted_index":{"Transfer":[0],"learning":[1,48],"(TL)":[2],"techniques,":[3],"which":[4],"leverage":[5],"prior":[6],"knowledge":[7],"gained":[8],"from":[9],"data":[10],"with":[11,98,141],"different":[12],"distributions":[13],"to":[14,37,105,129],"achieve":[15],"higher":[16],"performance":[17,75,153,192,197],"and":[18,29,58,67,82,95,115,117,121,137,163,175,186,193,201],"reduced":[19],"training":[20,56],"time,":[21],"are":[22,124],"often":[23],"used":[24],"in":[25,41,144],"computer":[26],"vision":[27],"(CV)":[28],"natural":[30],"language":[31],"processing":[32],"(NLP),":[33],"but":[34,166],"have":[35],"yet":[36],"be":[38],"fully":[39],"utilized":[40],"the":[42,55,62,158,161,169,173,181],"field":[43],"of":[44,172,183],"radio":[45,71],"frequency":[46,72,112],"machine":[47],"(RFML).":[49],"This":[50],"work":[51],"systematically":[52],"evaluates":[53],"how":[54,127],"domain":[57,135],"task,":[59],"characterized":[60],"by":[61],"transmitter":[63],"(Tx)/receiver":[64],"(Rx)":[65],"hardware":[66,187],"channel":[68,102,184],"environment,":[69],"impact":[70],"(RF)":[73],"TL":[74,132,152,191,196],"for":[76,134],"example":[77],"automatic":[78],"modulation":[79],"classification":[80],"(AMC)":[81],"specific":[83],"emitter":[84],"identification":[85],"(SEI)":[86],"use-cases.":[87],"Through":[88],"exhaustive":[89],"experimentation":[90],"using":[91,198],"carefully":[92],"curated":[93],"synthetic":[94],"captured":[96],"datasets":[97],"varying":[99],"signal":[100,104],"types,":[101,103],"noise":[106],"ratios":[107],"(SNRs),":[108],"carrier/center":[109],"frequencys":[110],"(CFs),":[111],"offsets":[113],"(FOs),":[114],"Tx":[116],"Rx":[118],"devices,":[119],"actionable":[120],"generalized":[122],"conclusions":[123],"drawn":[125],"regarding":[126],"best":[128],"use":[130],"RF":[131,151,190,195],"techniques":[133],"adaptation":[136],"sequential":[138],"learning.":[139],"Consistent":[140],"trends":[142],"identified":[143],"other":[145],"modalities,":[146],"our":[147],"results":[148],"show":[149],"that":[150],"is":[154],"highly":[155],"dependent":[156],"on":[157,168,189],"similarity":[159],"between":[160],"source":[162,174],"target":[164,176],"domains/tasks,":[165],"also":[167,179],"relative":[170],"difficulty":[171],"domains/tasks.":[177],"Results":[178],"discuss":[180],"impacts":[182],"environment":[185],"variations":[188],"compare":[194],"head":[199],"re-training":[200],"model":[202],"fine-tuning":[203],"methods.":[204]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
