{"id":"https://openalex.org/W4391128302","doi":"https://doi.org/10.1109/twc.2024.3354238","title":"Toward Better Low-Rate Deep Learning-Based CSI Feedback: A Test Channel-Based Approach","display_name":"Toward Better Low-Rate Deep Learning-Based CSI Feedback: A Test Channel-Based Approach","publication_year":2024,"publication_date":"2024-01-23","ids":{"openalex":"https://openalex.org/W4391128302","doi":"https://doi.org/10.1109/twc.2024.3354238"},"language":"en","primary_location":{"id":"doi:10.1109/twc.2024.3354238","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/twc.2024.3354238","pdf_url":null,"source":{"id":"https://openalex.org/S63459445","display_name":"IEEE Transactions on Wireless Communications","issn_l":"1536-1276","issn":["1536-1276","1558-2248"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Wireless Communications","raw_type":"journal-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/A5086203841","display_name":"Xin Liang","orcid":"https://orcid.org/0000-0003-4306-9836"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Liang","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4306-9836","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078730764","display_name":"Zhuqing Jia","orcid":"https://orcid.org/0000-0002-8329-9911"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuqing Jia","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8329-9911","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018769767","display_name":"Xinyu Gu","orcid":"https://orcid.org/0000-0001-6762-7463"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyu Gu","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6762-7463","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100351907","display_name":"Lin Zhang","orcid":"https://orcid.org/0000-0003-0424-9965"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]},{"id":"https://openalex.org/I4210135108","display_name":"Beijing Municipal Health Commission","ror":"https://ror.org/0374a5s68","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210110145","https://openalex.org/I4210135108"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Zhang","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","Beijing Big Data Center, Beijing Municipal Bureau of Economy and Information Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0424-9965","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Beijing Big Data Center, Beijing Municipal Bureau of Economy and Information Technology, Beijing, China","institution_ids":["https://openalex.org/I4210135108"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.6436,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.90079464,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"23","issue":"8","first_page":"8773","last_page":"8786"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11032","display_name":"VLSI and Analog Circuit Testing","score":0.9886999726295471,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T11032","display_name":"VLSI and Analog Circuit Testing","score":0.9886999726295471,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T10320","display_name":"Neural Networks and Applications","score":0.9139000177383423,"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":0.7125037908554077},{"id":"https://openalex.org/keywords/test","display_name":"Test (biology)","score":0.48171311616897583},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.41910573840141296},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4107918441295624},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36980146169662476},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3327462673187256},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.2844696044921875}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7125037908554077},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.48171311616897583},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.41910573840141296},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4107918441295624},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36980146169662476},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3327462673187256},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.2844696044921875},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/twc.2024.3354238","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/twc.2024.3354238","pdf_url":null,"source":{"id":"https://openalex.org/S63459445","display_name":"IEEE Transactions on Wireless Communications","issn_l":"1536-1276","issn":["1536-1276","1558-2248"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Wireless Communications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3818096061","display_name":null,"funder_award_id":"62201080","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4512597287","display_name":null,"funder_award_id":"62201089","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6556914460","display_name":null,"funder_award_id":"2022YFF0610303","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1995562189","https://openalex.org/W2072079297","https://openalex.org/W2094655360","https://openalex.org/W2099111195","https://openalex.org/W2104250811","https://openalex.org/W2109808436","https://openalex.org/W2124287399","https://openalex.org/W2134383396","https://openalex.org/W2156801811","https://openalex.org/W2288660273","https://openalex.org/W2290865102","https://openalex.org/W2320454118","https://openalex.org/W2624494617","https://openalex.org/W2750570659","https://openalex.org/W2886124254","https://openalex.org/W2911910187","https://openalex.org/W2914338909","https://openalex.org/W2963037989","https://openalex.org/W2963145597","https://openalex.org/W3003580643","https://openalex.org/W3003689553","https://openalex.org/W3020027015","https://openalex.org/W3024777099","https://openalex.org/W3045610411","https://openalex.org/W3058289049","https://openalex.org/W3096337450","https://openalex.org/W3158832185","https://openalex.org/W3167779900","https://openalex.org/W3176353501","https://openalex.org/W3186801274","https://openalex.org/W3199651421","https://openalex.org/W3200156946","https://openalex.org/W3203416629","https://openalex.org/W3206045524","https://openalex.org/W4221143069","https://openalex.org/W4221153184","https://openalex.org/W4225725726","https://openalex.org/W4289536575","https://openalex.org/W4290994016","https://openalex.org/W4295872522","https://openalex.org/W4312045171","https://openalex.org/W4312050602","https://openalex.org/W4312743881","https://openalex.org/W6769243733","https://openalex.org/W6847032966"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4313488044","https://openalex.org/W3209574120","https://openalex.org/W4312192474","https://openalex.org/W4210805261"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"(DL)-based":[2],"channel":[3,64],"state":[4],"information":[5,75],"(CSI)":[6],"feedback":[7,39,88,101,109,139,147,163],"provides":[8],"satisfactory":[9],"reconstruction":[10,183],"accuracy":[11],"of":[12,28,36,54,68,99,106,119,130,136,145,153,170,176],"downlink":[13],"CSI":[14,38,87,100,108,138,162],"for":[15,85,142,161],"the":[16,26,32,41,48,52,62,92,96,104,107,117,123,128,131,137,143,151,167,181,189,198],"base":[17],"station":[18],"in":[19,47,72,95,122,197],"massive":[20],"multiple-input":[21],"multiple-output":[22],"(MIMO)":[23],"systems.":[24],"Although":[25],"introduction":[27],"codeword":[29,171,177,194],"quantization":[30,71,82,174,195],"improves":[31],"efficiency":[33],"and":[34,133,173],"feasibility":[35],"DL-based":[37,86],"networks,":[40],"gradient":[42,93],"problem":[43,94],"caused":[44],"by":[45,60],"quantizers":[46],"training":[49,98,105,132,159],"stage":[50],"compromises":[51],"performance":[53],"neural":[55],"networks.":[56,102],"In":[57],"this":[58],"paper,":[59],"considering":[61],"test":[63,80],"as":[65],"an":[66,157],"equivalent":[67],"ideal":[69],"rate-distortion":[70],"a":[73,79],"mutual":[74],"sense,":[76],"we":[77,155],"propose":[78,156],"channel-based":[81],"module":[83],"(TCQM)":[84],"networks":[89,164],"which":[90,126],"mitigates":[91],"end-to-end":[97],"Moreover,":[103],"network":[110],"with":[111],"TCQM":[112],"is":[113],"not":[114],"dependent":[115],"on":[116,150],"design":[118,134],"practical":[120],"quantizer":[121],"inference":[124],"stage,":[125],"reduces":[127],"complexity":[129],"constraints":[135],"system.":[140],"Finally,":[141],"setting":[144],"fixed":[146],"overhead,":[148],"based":[149],"idea":[152],"TCQM,":[154],"adaptive":[158],"strategy":[160],"to":[165,179],"evaluate":[166],"proper":[168],"combination":[169],"length":[172],"rate":[175],"elements":[178],"achieve":[180],"optimal":[182],"accuracy.":[184],"Experiment":[185],"results":[186],"show":[187],"that":[188],"proposed":[190],"schemes":[191,196],"outperform":[192],"existing":[193],"literature.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2026-06-13T07:54:00.901334","created_date":"2024-01-24T00:00:00"}
