{"id":"https://openalex.org/W4293057712","doi":"https://doi.org/10.1109/vtc2022-spring54318.2022.9860821","title":"Online Compressive Channel Learning Using Untrained Deep Generative Model","display_name":"Online Compressive Channel Learning Using Untrained Deep Generative Model","publication_year":2022,"publication_date":"2022-06-01","ids":{"openalex":"https://openalex.org/W4293057712","doi":"https://doi.org/10.1109/vtc2022-spring54318.2022.9860821"},"language":"en","primary_location":{"id":"doi:10.1109/vtc2022-spring54318.2022.9860821","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2022-spring54318.2022.9860821","pdf_url":null,"source":{"id":"https://openalex.org/S4363607744","display_name":"2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)","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/A5100341636","display_name":"Ben Wang","orcid":"https://orcid.org/0000-0002-5341-1642"},"institutions":[{"id":"https://openalex.org/I30809798","display_name":"ShanghaiTech University","ror":"https://ror.org/030bhh786","country_code":"CN","type":"education","lineage":["https://openalex.org/I30809798"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]},{"id":"https://openalex.org/I4210147322","display_name":"Shanghai Institute of Microsystem and Information Technology","ror":"https://ror.org/04nytyj38","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210147322"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ben Wang","raw_affiliation_strings":["ShanghaiTech University,School of Information Science and Technology,Shanghai,China,201210","Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, China","University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"ShanghaiTech University,School of Information Science and Technology,Shanghai,China,201210","institution_ids":["https://openalex.org/I30809798"]},{"raw_affiliation_string":"Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, China","institution_ids":["https://openalex.org/I4210147322"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005274577","display_name":"Lixiang Lian","orcid":"https://orcid.org/0000-0002-9958-7264"},"institutions":[{"id":"https://openalex.org/I30809798","display_name":"ShanghaiTech University","ror":"https://ror.org/030bhh786","country_code":"CN","type":"education","lineage":["https://openalex.org/I30809798"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lixiang Lian","raw_affiliation_strings":["ShanghaiTech University,School of Information Science and Technology,Shanghai,China,201210"],"affiliations":[{"raw_affiliation_string":"ShanghaiTech University,School of Information Science and Technology,Shanghai,China,201210","institution_ids":["https://openalex.org/I30809798"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100341636"],"corresponding_institution_ids":["https://openalex.org/I30809798","https://openalex.org/I4210147322","https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0813941,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"27","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9994999766349792,"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":0.9994999766349792,"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/T12791","display_name":"Full-Duplex Wireless Communications","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.9883000254631042,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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":0.8112431168556213},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.7253435850143433},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6430193185806274},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.6168679594993591},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6062496304512024},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5833470821380615},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.543428361415863},{"id":"https://openalex.org/keywords/mimo","display_name":"MIMO","score":0.5345916152000427},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49941587448120117},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4622611105442047},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43140357732772827},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37897902727127075}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8112431168556213},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.7253435850143433},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6430193185806274},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.6168679594993591},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6062496304512024},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5833470821380615},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.543428361415863},{"id":"https://openalex.org/C207987634","wikidata":"https://www.wikidata.org/wiki/Q176862","display_name":"MIMO","level":3,"score":0.5345916152000427},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49941587448120117},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4622611105442047},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43140357732772827},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37897902727127075},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtc2022-spring54318.2022.9860821","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2022-spring54318.2022.9860821","pdf_url":null,"source":{"id":"https://openalex.org/S4363607744","display_name":"2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)","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":18,"referenced_works":["https://openalex.org/W1997834106","https://openalex.org/W2519091744","https://openalex.org/W2808361645","https://openalex.org/W2810871807","https://openalex.org/W2911644454","https://openalex.org/W2938113616","https://openalex.org/W2963190722","https://openalex.org/W2963684088","https://openalex.org/W2997109520","https://openalex.org/W3020652653","https://openalex.org/W3081258430","https://openalex.org/W3081455451","https://openalex.org/W3111067891","https://openalex.org/W3171808082","https://openalex.org/W4320013936","https://openalex.org/W6685352114","https://openalex.org/W6746466744","https://openalex.org/W6796703771"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W4312814274","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W2353836703"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"an":[5],"untrained":[6],"deep":[7,21,48],"image":[8],"prior-based":[9],"compressive":[10,136],"channel":[11,61,92],"estimation":[12],"(CE)":[13],"algorithm":[14],"in":[15],"massive":[16,32],"MIMO":[17,33],"systems.":[18],"We":[19,99],"adopt":[20],"convolutional":[22],"generative":[23],"network":[24],"(DCGN)":[25],"to":[26,40,79,83,106],"learn":[27],"the":[28,42,76,84,91,108,112,122],"sparse":[29],"prior":[30,97],"of":[31],"channels,":[34],"whose":[35],"weights":[36],"can":[37,70,89,125],"be":[38,71],"optimized":[39],"match":[41],"compressed":[43],"measurements.":[44],"Unlike":[45],"various":[46],"supervised":[47],"neural":[49],"networks":[50],"(DNN)-based":[51],"CE":[52,109,132],"algorithms,":[53],"which":[54],"require":[55,67],"offline":[56],"training":[57],"over":[58],"large":[59],"simulated":[60],"datasets,":[62],"our":[63],"DCGN":[64,88],"does":[65],"not":[66],"pre-training":[68],"and":[69,116,129,139],"learned":[72,103],"online":[73,131],"based":[74],"on":[75],"real-time":[77],"measurements":[78,113],"make":[80],"it":[81],"adaptable":[82],"time-varying":[85],"channels.":[86],"Besides,":[87],"exploit":[90],"structure":[93],"automatically":[94],"without":[95],"any":[96],"knowledge.":[98],"further":[100],"devise":[101],"a":[102],"regularization":[104],"technique":[105],"improve":[107],"performance":[110,133],"when":[111],"are":[114],"noisy":[115],"highly":[117],"compressive.":[118],"Simulations":[119],"show":[120],"that":[121],"proposed":[123],"method":[124],"achieve":[126],"more":[127],"accurate":[128],"robust":[130],"than":[134],"traditional":[135],"sensing":[137],"(CS)":[138],"DNN-based":[140],"methods.":[141]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}