{"id":"https://openalex.org/W4414539198","doi":"https://doi.org/10.1109/icc52391.2025.11161432","title":"Low-Overhead Near-Field Beam Training Based on Bayesian Regression","display_name":"Low-Overhead Near-Field Beam Training Based on Bayesian Regression","publication_year":2025,"publication_date":"2025-06-08","ids":{"openalex":"https://openalex.org/W4414539198","doi":"https://doi.org/10.1109/icc52391.2025.11161432"},"language":"en","primary_location":{"id":"doi:10.1109/icc52391.2025.11161432","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc52391.2025.11161432","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2025 - IEEE International Conference on Communications","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/A5104216174","display_name":"Zhuo Xu","orcid":"https://orcid.org/0000-0001-8178-3307"},"institutions":[{"id":"https://openalex.org/I4210148107","display_name":"Space Engineering University","ror":"https://ror.org/04rj1td02","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210148107"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuo Xu","raw_affiliation_strings":["Tsinghua University,State Key Laboratory of Space Network and Communications,Department of Electronic Engineering,Beijing,China,100084"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,State Key Laboratory of Space Network and Communications,Department of Electronic Engineering,Beijing,China,100084","institution_ids":["https://openalex.org/I4210148107","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100337466","display_name":"Zijian Zhang","orcid":"https://orcid.org/0000-0003-1194-8334"},"institutions":[{"id":"https://openalex.org/I4210148107","display_name":"Space Engineering University","ror":"https://ror.org/04rj1td02","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210148107"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zijian Zhang","raw_affiliation_strings":["Tsinghua University,State Key Laboratory of Space Network and Communications,Department of Electronic Engineering,Beijing,China,100084"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,State Key Laboratory of Space Network and Communications,Department of Electronic Engineering,Beijing,China,100084","institution_ids":["https://openalex.org/I4210148107","https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081869164","display_name":"Linglong Dai","orcid":"https://orcid.org/0000-0002-4250-7315"},"institutions":[{"id":"https://openalex.org/I4210148107","display_name":"Space Engineering University","ror":"https://ror.org/04rj1td02","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210148107"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linglong Dai","raw_affiliation_strings":["Tsinghua University,State Key Laboratory of Space Network and Communications,Department of Electronic Engineering,Beijing,China,100084"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,State Key Laboratory of Space Network and Communications,Department of Electronic Engineering,Beijing,China,100084","institution_ids":["https://openalex.org/I4210148107","https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":"1724","last_page":"1729"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11946","display_name":"Antenna Design and Optimization","score":0.9787999987602234,"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"}},"topics":[{"id":"https://openalex.org/T11946","display_name":"Antenna Design and Optimization","score":0.9787999987602234,"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"}},{"id":"https://openalex.org/T14158","display_name":"Optical Systems and Laser Technology","score":0.9750999808311462,"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/T12450","display_name":"Radio Astronomy Observations and Technology","score":0.902400016784668,"subfield":{"id":"https://openalex.org/subfields/3103","display_name":"Astronomy and Astrophysics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5922999978065491},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5859000086784363},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.527899980545044},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.47350001335144043},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.46129998564720154},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.45590001344680786},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3799999952316284},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.37529999017715454}],"concepts":[{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5922999978065491},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5859000086784363},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.559499979019165},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.527899980545044},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.47350001335144043},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.46129998564720154},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.45590001344680786},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42100000381469727},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40630000829696655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3959999978542328},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3799999952316284},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3765000104904175},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.37529999017715454},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3596000075340271},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3352999985218048},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.321399986743927},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3003000020980835},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3003000020980835},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C19889080","wikidata":"https://www.wikidata.org/wiki/Q2835852","display_name":"Beam search","level":3,"score":0.2669999897480011},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.2635999917984009}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icc52391.2025.11161432","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc52391.2025.11161432","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2025 - IEEE International Conference on Communications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2858362410","display_name":null,"funder_award_id":"62325106","funder_id":"https://openalex.org/F4320336125","funder_display_name":"National Science Fund for Distinguished Young Scholars"}],"funders":[{"id":"https://openalex.org/F4320336125","display_name":"National Science Fund for Distinguished Young Scholars","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2166566250","https://openalex.org/W4226370247","https://openalex.org/W4297094903","https://openalex.org/W4297310224","https://openalex.org/W4311414687","https://openalex.org/W4312495751","https://openalex.org/W4385819700","https://openalex.org/W4386472851","https://openalex.org/W4387790002","https://openalex.org/W4389924236","https://openalex.org/W4390496060","https://openalex.org/W4405563700"],"related_works":[],"abstract_inverted_index":{"In":[0,58],"extremely":[1],"large-scale":[2],"multiple":[3,5],"input":[4],"output":[6],"(XL-MIMO)":[7],"systems,":[8],"near-field":[9,35,39,56,66,76,96,158],"beam":[10,40,67,159],"training":[11,25,41,68,81,146,160,166],"is":[12],"an":[13],"essential":[14],"way":[15],"to":[16,78,94],"acquire":[17],"channel":[18],"state":[19],"information.":[20],"To":[21],"reduce":[22,79],"the":[23,29,34,46,52,73,80,90,104,110,115,139,144,151,154,165,170],"high":[24,145,171],"overhead":[26,167],"brought":[27],"by":[28,85,107],"additional":[30],"distance":[31],"dimension":[32],"of":[33,138,153],"codebook,":[36,141],"some":[37],"overhead-reduced":[38],"schemes":[42,50],"were":[43],"proposed":[44,128,155],"in":[45],"literature.":[47],"However,":[48],"existing":[49],"ignore":[51],"correlation":[53,74],"between":[54,75],"different":[55,95,120],"beams.":[57],"this":[59],"paper,":[60],"we":[61,88],"propose":[62],"a":[63,99,134],"Bayesian":[64,86,156],"regression-based":[65,157],"scheme,":[69,161],"which":[70,142,162],"fully":[71],"utilizes":[72],"code-words":[77],"overhead.":[82,147],"Specifically,":[83],"inspired":[84],"regression,":[87],"model":[89],"received":[91],"signal":[92],"corresponding":[93],"codewords":[97,136],"as":[98],"Gaussian":[100],"process":[101],"and":[102,113,125],"determine":[103],"optimal":[105],"codeword":[106,116],"iteratively":[108],"updating":[109],"posterior":[111],"distribution":[112],"designing":[114],"searching":[117,121,132],"order.":[118],"Besides,":[119],"strategies":[122],"are":[123],"analysed":[124],"compared.":[126],"The":[127],"scheme":[129],"only":[130],"requires":[131],"for":[133],"few":[135],"instead":[137],"entire":[140],"reduces":[143,164],"Simulation":[148],"results":[149],"verify":[150],"effectiveness":[152],"significantly":[163],"while":[168],"maintaining":[169],"achievable":[172],"rate":[173],"performance.":[174]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}