{"id":"https://openalex.org/W4361790897","doi":"https://doi.org/10.1145/3582197.3582221","title":"A 2layer-BiLSTM-Attention-Based Method for Modulation Recognition of Communication Signals","display_name":"A 2layer-BiLSTM-Attention-Based Method for Modulation Recognition of Communication Signals","publication_year":2022,"publication_date":"2022-12-23","ids":{"openalex":"https://openalex.org/W4361790897","doi":"https://doi.org/10.1145/3582197.3582221"},"language":"en","primary_location":{"id":"doi:10.1145/3582197.3582221","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3582197.3582221","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 10th International Conference on Information Technology: IoT and Smart City","raw_type":"proceedings-article"},"type":"conference-paper","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/A5024302274","display_name":"Zihao Song","orcid":"https://orcid.org/0000-0001-8805-5353"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zihao Song","raw_affiliation_strings":["Early Warning Academy, China"],"raw_orcid":"https://orcid.org/0000-0001-8805-5353","affiliations":[{"raw_affiliation_string":"Early Warning Academy, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030894279","display_name":"Wei Cheng","orcid":"https://orcid.org/0000-0002-0819-292X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Cheng","raw_affiliation_strings":["Early Warning Academy, China"],"raw_orcid":"https://orcid.org/0000-0002-0819-292X","affiliations":[{"raw_affiliation_string":"Early Warning Academy, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059165046","display_name":"Xiaobai Li","orcid":"https://orcid.org/0000-0002-9009-3153"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiaobai Li","raw_affiliation_strings":["Early Warning Academy, China"],"raw_orcid":"https://orcid.org/0000-0002-9009-3153","affiliations":[{"raw_affiliation_string":"Early Warning Academy, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058299958","display_name":"Bingjie Zeng","orcid":"https://orcid.org/0000-0002-9479-9647"},"institutions":[{"id":"https://openalex.org/I4210092582","display_name":"Guizhou Academy of Agricultural Sciences","ror":"https://ror.org/00ev3nz67","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210092582"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bingjie Zeng","raw_affiliation_strings":["PAP Guizhou Crops, China"],"raw_orcid":"https://orcid.org/0000-0002-9479-9647","affiliations":[{"raw_affiliation_string":"PAP Guizhou Crops, China","institution_ids":["https://openalex.org/I4210092582"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"146","last_page":"152"},"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":0.9372000098228455,"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":0.790880560874939},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.738080620765686},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7327326536178589},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6492528915405273},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6341216564178467},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5953742265701294},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5329374074935913},{"id":"https://openalex.org/keywords/modulation","display_name":"Modulation (music)","score":0.5041004419326782},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.45612847805023193},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.33483409881591797}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.790880560874939},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.738080620765686},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7327326536178589},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6492528915405273},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6341216564178467},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5953742265701294},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5329374074935913},{"id":"https://openalex.org/C123079801","wikidata":"https://www.wikidata.org/wiki/Q750240","display_name":"Modulation (music)","level":2,"score":0.5041004419326782},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.45612847805023193},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.33483409881591797},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3582197.3582221","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3582197.3582221","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 10th International Conference on Information Technology: IoT and Smart City","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W2272847350","https://openalex.org/W2884089434","https://openalex.org/W2917006683","https://openalex.org/W2963809753"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4230611425","https://openalex.org/W2731899572","https://openalex.org/W4294635752","https://openalex.org/W4304166257","https://openalex.org/W4383066092","https://openalex.org/W3215138031","https://openalex.org/W2804383999","https://openalex.org/W2802049774"],"abstract_inverted_index":{"Aiming":[0],"at":[1,86,91],"the":[2,17,28,45,50,62,65,73],"problems":[3],"that":[4],"feature":[5],"extraction":[6],"accuracy":[7,19,70],"of":[8,20,33],"feature-based":[9],"modulation":[10],"recognition":[11,18,41,69,83],"methods":[12,24,37],"is":[13,25,38,53],"difficult":[14],"to":[15],"guarantee,":[16],"one-dimensional-feature-based":[21],"deep":[22,35,96],"learning":[23,36,97],"low,":[26],"and":[27,30,76,89],"time":[29],"storage":[31],"overhead":[32],"high-dimensional-feature-based":[34],"large,":[39],"a":[40],"method":[42,66],"based":[43,60],"on":[44,61],"two-layer":[46],"BiLSTM":[47],"followed":[48],"by":[49],"attention":[51],"layer":[52],"proposed.":[54],"The":[55,82],"experiment":[56],"results":[57],"show":[58],"that,":[59],"RadioML2016.10a":[63],"dataset,":[64],"achieves":[67],"high":[68],"without":[71],"increasing":[72],"training":[74],"burden":[75],"extracting":[77],"features":[78],"from":[79],"original":[80],"signals.":[81],"reaches":[84],"86.7%":[85],"0":[87],"dB":[88],"91.5%":[90],"18":[92],"dB,":[93],"outperforming":[94],"other":[95],"methods.":[98]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
