{"id":"https://openalex.org/W4402158007","doi":"https://doi.org/10.1109/icc51166.2024.10622182","title":"Learning Based CSI Look Up Table: A Novel Vector Quantization Approach for High Accuracy CSI Reconstruction","display_name":"Learning Based CSI Look Up Table: A Novel Vector Quantization Approach for High Accuracy CSI Reconstruction","publication_year":2024,"publication_date":"2024-06-09","ids":{"openalex":"https://openalex.org/W4402158007","doi":"https://doi.org/10.1109/icc51166.2024.10622182"},"language":"en","primary_location":{"id":"doi:10.1109/icc51166.2024.10622182","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icc51166.2024.10622182","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2024 - IEEE International Conference on Communications","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/A5011748948","display_name":"Baoling Sheen","orcid":"https://orcid.org/0000-0002-0222-2454"},"institutions":[{"id":"https://openalex.org/I4210146936","display_name":"Huawei Technologies (United States)","ror":"https://ror.org/03jyqk712","country_code":"US","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210146936"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Baoling Sheen","raw_affiliation_strings":["Wireless Research and Standards, Futurewei Technologies,Bridgewater,NJ,USA,08807"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wireless Research and Standards, Futurewei Technologies,Bridgewater,NJ,USA,08807","institution_ids":["https://openalex.org/I4210146936"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046715321","display_name":"Yifei Song","orcid":"https://orcid.org/0000-0002-4198-7839"},"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":"Yifei Song","raw_affiliation_strings":["Virginia Tech,Bradley Department of Electrical and Computer Engineering,Blacksburg,VA,USA,24060"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Virginia Tech,Bradley Department of Electrical and Computer Engineering,Blacksburg,VA,USA,24060","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084819904","display_name":"Juan Roa","orcid":"https://orcid.org/0000-0002-8970-421X"},"institutions":[{"id":"https://openalex.org/I4210146936","display_name":"Huawei Technologies (United States)","ror":"https://ror.org/03jyqk712","country_code":"US","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210146936"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Juan Roa","raw_affiliation_strings":["Wireless Research and Standards, Futurewei Technologies,Bridgewater,NJ,USA,08807"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wireless Research and Standards, Futurewei Technologies,Bridgewater,NJ,USA,08807","institution_ids":["https://openalex.org/I4210146936"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036860907","display_name":"Zhigang Rong","orcid":null},"institutions":[{"id":"https://openalex.org/I4210146936","display_name":"Huawei Technologies (United States)","ror":"https://ror.org/03jyqk712","country_code":"US","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210146936"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhigang Rong","raw_affiliation_strings":["Wireless Research and Standards, Futurewei Technologies,Bridgewater,NJ,USA,08807"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wireless Research and Standards, Futurewei Technologies,Bridgewater,NJ,USA,08807","institution_ids":["https://openalex.org/I4210146936"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048324605","display_name":"Renjian Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210146936","display_name":"Huawei Technologies (United States)","ror":"https://ror.org/03jyqk712","country_code":"US","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210146936"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Renjian Zhao","raw_affiliation_strings":["Wireless Research and Standards, Futurewei Technologies,Bridgewater,NJ,USA,08807"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wireless Research and Standards, Futurewei Technologies,Bridgewater,NJ,USA,08807","institution_ids":["https://openalex.org/I4210146936"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026768725","display_name":"Weimin Xiao","orcid":"https://orcid.org/0009-0002-9897-1923"},"institutions":[{"id":"https://openalex.org/I4210146936","display_name":"Huawei Technologies (United States)","ror":"https://ror.org/03jyqk712","country_code":"US","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210146936"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weimin Xiao","raw_affiliation_strings":["Wireless Research and Standards, Futurewei Technologies,Bridgewater,NJ,USA,08807"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wireless Research and Standards, Futurewei Technologies,Bridgewater,NJ,USA,08807","institution_ids":["https://openalex.org/I4210146936"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5011748948"],"corresponding_institution_ids":["https://openalex.org/I4210146936"],"apc_list":null,"apc_paid":null,"fwci":0.1975,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.50511258,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"30","issue":null,"first_page":"2561","last_page":"2566"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":0.9984999895095825,"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"}},"topics":[{"id":"https://openalex.org/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":0.9984999895095825,"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/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/vector-quantization","display_name":"Vector quantization","score":0.7877483367919922},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6877737641334534},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.5816542506217957},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5402623414993286},{"id":"https://openalex.org/keywords/table","display_name":"Table (database)","score":0.4877703785896301},{"id":"https://openalex.org/keywords/linde\u2013buzo\u2013gray-algorithm","display_name":"Linde\u2013Buzo\u2013Gray algorithm","score":0.46308809518814087},{"id":"https://openalex.org/keywords/learning-vector-quantization","display_name":"Learning vector quantization","score":0.43567946553230286},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4261942505836487},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36717700958251953},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3546290397644043},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.20377081632614136}],"concepts":[{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.7877483367919922},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6877737641334534},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.5816542506217957},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5402623414993286},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.4877703785896301},{"id":"https://openalex.org/C93372532","wikidata":"https://www.wikidata.org/wiki/Q6552455","display_name":"Linde\u2013Buzo\u2013Gray algorithm","level":3,"score":0.46308809518814087},{"id":"https://openalex.org/C40567965","wikidata":"https://www.wikidata.org/wiki/Q1820283","display_name":"Learning vector quantization","level":3,"score":0.43567946553230286},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4261942505836487},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36717700958251953},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3546290397644043},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.20377081632614136}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icc51166.2024.10622182","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icc51166.2024.10622182","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2024 - IEEE International Conference on Communications","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/W1518945650","https://openalex.org/W2074120933","https://openalex.org/W2099721949","https://openalex.org/W2141682101","https://openalex.org/W2752796333","https://openalex.org/W2798309714","https://openalex.org/W2886124254","https://openalex.org/W2963799213","https://openalex.org/W2966038277","https://openalex.org/W3037818400","https://openalex.org/W3168953114","https://openalex.org/W4210525872","https://openalex.org/W4289536575","https://openalex.org/W4312667604","https://openalex.org/W4321472097","https://openalex.org/W4327928761","https://openalex.org/W4385574803","https://openalex.org/W6849855581"],"related_works":["https://openalex.org/W2352648934","https://openalex.org/W2378212145","https://openalex.org/W2145213141","https://openalex.org/W2100968651","https://openalex.org/W2158420043","https://openalex.org/W2071781501","https://openalex.org/W4243803532","https://openalex.org/W1805748654","https://openalex.org/W3193872944","https://openalex.org/W2142248489"],"abstract_inverted_index":{"To":[0],"fully":[1],"exploit":[2],"the":[3,18,64,102,111,157,170],"benefits":[4],"of":[5,20,123,161,173],"spatial":[6],"multiplexing":[7],"gains":[8],"within":[9],"Frequency":[10],"Division":[11],"Duplex":[12],"(FDD)":[13],"Multiple-Input":[14],"Multiple-Output":[15],"(MIMO)":[16],"systems,":[17,166],"development":[19],"a":[21,86,152],"robust":[22],"Channel":[23],"State":[24],"Information":[25],"(CSI)":[26],"feedback":[27,163],"compression":[28],"methodology":[29,44],"with":[30],"low":[31],"over-":[32],"the-air":[33],"CSI":[34,94,112,124,162],"overhead":[35,52,77,125],"while":[36,130],"achieving":[37,132],"high":[38],"reconstruction":[39,135],"accuracy":[40,136],"is":[41],"critical.":[42],"Such":[43],"must":[45],"be":[46],"able":[47],"to":[48,74,169],"effectively":[49],"reduce":[50],"communication":[51],"without":[53],"compromising":[54],"system":[55,138],"level":[56,139],"performance.":[57],"Traditional":[58],"codebook-based":[59,143],"approach,":[60],"as":[61,101],"outlined":[62],"in":[63,118,155,164],"3rd":[65],"Generation":[66],"Partnership":[67],"Project":[68],"(3GPP)":[69],"standards":[70],"encounters":[71],"significant":[72],"challenges":[73],"balance":[75],"air-interface":[76],"and":[78,88,137,159,175],"computational":[79],"complexity.":[80],"In":[81],"this":[82],"paper,":[83],"we":[84],"introduce":[85],"novel":[87],"adaptable":[89],"Deep":[90],"Leaning":[91],"(DL)":[92],"based":[93],"codebook":[95],"technique":[96],"leveraging":[97],"vector":[98],"quantization":[99],"known":[100],"CSI-Look-Up":[103],"Table":[104],"(CSI-LUT).":[105],"Our":[106],"numerical":[107],"results":[108],"show":[109],"that":[110,147],"-":[113],"L":[114],"UT":[115],"has":[116],"potential":[117],"reducing":[119],">":[120],"90":[121],"%":[122],"for":[126],"max":[127],"rank":[128],"1":[129],"still":[131],"better":[133],"average":[134],"performance":[140,160],"than":[141],"traditional":[142],"approach.":[144],"We":[145],"anticipate":[146],"such":[148],"advancements":[149],"will":[150],"play":[151],"pivotal":[153],"role":[154],"enhancing":[156],"efficiency":[158],"MIMO":[165],"contributing":[167],"significantly":[168],"evolving":[171],"landscape":[172],"5G":[174],"beyond.":[176]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}