{"id":"https://openalex.org/W4389421200","doi":"https://doi.org/10.48550/arxiv.2312.02298","title":"MoE-AMC: Enhancing Automatic Modulation Classification Performance Using Mixture-of-Experts","display_name":"MoE-AMC: Enhancing Automatic Modulation Classification Performance Using Mixture-of-Experts","publication_year":2023,"publication_date":"2023-12-04","ids":{"openalex":"https://openalex.org/W4389421200","doi":"https://doi.org/10.48550/arxiv.2312.02298"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2312.02298","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2312.02298","pdf_url":"https://arxiv.org/pdf/2312.02298","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2312.02298","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101865273","display_name":"Jiaxin Gao","orcid":"https://orcid.org/0009-0001-2530-8808"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Jiaxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084669485","display_name":"Qinglong Cao","orcid":"https://orcid.org/0000-0003-2742-026X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Qinglong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101707444","display_name":"Yuntian Chen","orcid":"https://orcid.org/0000-0003-4566-8197"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yuntian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9997000098228455,"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":0.9997000098228455,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.9736999869346619,"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":0.5757123827934265},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.5118514895439148},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4856909215450287},{"id":"https://openalex.org/keywords/modulation","display_name":"Modulation (music)","score":0.48244816064834595},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4335087835788727},{"id":"https://openalex.org/keywords/signal-to-noise-ratio","display_name":"Signal-to-noise ratio (imaging)","score":0.4138079285621643},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39761972427368164},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.36857303977012634},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3578186333179474},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3556968569755554},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.2719809412956238},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2676606774330139},{"id":"https://openalex.org/keywords/acoustics","display_name":"Acoustics","score":0.07727247476577759}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5757123827934265},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.5118514895439148},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4856909215450287},{"id":"https://openalex.org/C123079801","wikidata":"https://www.wikidata.org/wiki/Q750240","display_name":"Modulation (music)","level":2,"score":0.48244816064834595},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4335087835788727},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.4138079285621643},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39761972427368164},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.36857303977012634},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3578186333179474},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3556968569755554},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.2719809412956238},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2676606774330139},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.07727247476577759},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2312.02298","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2312.02298","pdf_url":"https://arxiv.org/pdf/2312.02298","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2312.02298","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2312.02298","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2312.02298","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2312.02298","pdf_url":"https://arxiv.org/pdf/2312.02298","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4389421200.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2351655225","https://openalex.org/W2183768935","https://openalex.org/W2004123705","https://openalex.org/W2361982570","https://openalex.org/W289407349","https://openalex.org/W3007307084","https://openalex.org/W3208739727","https://openalex.org/W946106684","https://openalex.org/W4308823735","https://openalex.org/W2787240389"],"abstract_inverted_index":{"Automatic":[0],"Modulation":[1],"Classification":[2],"(AMC)":[3],"plays":[4],"a":[5,65,77,168,180],"vital":[6],"role":[7],"in":[8,29,40,53,56,76,120,126,174],"time":[9],"series":[10],"analysis,":[11],"such":[12],"as":[13],"signal":[14,129,185],"classification":[15,147,186],"and":[16,47,103],"identification":[17],"within":[18,188],"wireless":[19,189],"communications.":[20],"Deep":[21],"learning-based":[22],"AMC":[23,34,75],"models":[24,35,161],"have":[25],"demonstrated":[26],"significant":[27],"potential":[28],"this":[30,60],"domain.":[31],"However,":[32],"current":[33],"inadequately":[36],"consider":[37],"the":[38,85,91,139,156,175],"disparities":[39],"handling":[41,99],"signals":[42,102],"under":[43,131],"conditions":[44],"of":[45,93,149,158,171,177],"low":[46,100],"high":[48,109],"Signal-to-Noise":[49],"Ratio":[50],"(SNR),":[51],"resulting":[52],"an":[54,145],"unevenness":[55],"their":[57],"performance.":[58],"In":[59],"study,":[61],"we":[62],"propose":[63],"MoE-AMC,":[64],"novel":[66],"Mixture-of-Experts":[67],"(MoE)":[68],"based":[69],"model":[70],"specifically":[71],"crafted":[72],"to":[73,116],"address":[74],"well-balanced":[78],"manner":[79],"across":[80,151],"varying":[81],"SNR":[82,101,110,133,153],"conditions.":[83],"Utilizing":[84],"MoE":[86,172],"framework,":[87],"MoE-AMC":[88,115,143],"seamlessly":[89],"combines":[90],"strengths":[92],"LSRM":[94],"(a":[95,105],"Transformer-based":[96],"model)":[97,107],"for":[98,108,183],"HSRM":[104],"ResNet-based":[106],"signals.":[111],"This":[112,165],"integration":[113],"empowers":[114],"achieve":[117],"leading":[118],"performance":[119,157],"modulation":[121],"classification,":[122],"showcasing":[123],"its":[124],"efficacy":[125],"capturing":[127],"distinctive":[128],"features":[130],"diverse":[132],"scenarios.":[134],"We":[135],"conducted":[136],"experiments":[137],"using":[138],"RML2018.01a":[140],"dataset,":[141],"where":[142],"achieved":[144],"average":[146],"accuracy":[148,187],"71.76%":[150],"different":[152],"levels,":[154],"surpassing":[155],"previous":[159],"SOTA":[160],"by":[162],"nearly":[163],"10%.":[164],"study":[166],"represents":[167],"pioneering":[169],"application":[170],"techniques":[173],"realm":[176],"AMC,":[178],"offering":[179],"promising":[181],"avenue":[182],"elevating":[184],"communication":[190],"systems.":[191]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}
