{"id":"https://openalex.org/W4318141871","doi":"https://doi.org/10.1109/tcomm.2023.3239927","title":"Beam Training and Tracking With Limited Sampling Sets: Exploiting Environment Priors","display_name":"Beam Training and Tracking With Limited Sampling Sets: Exploiting Environment Priors","publication_year":2023,"publication_date":"2023-01-26","ids":{"openalex":"https://openalex.org/W4318141871","doi":"https://doi.org/10.1109/tcomm.2023.3239927"},"language":"en","primary_location":{"id":"doi:10.1109/tcomm.2023.3239927","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/tcomm.2023.3239927","pdf_url":null,"source":{"id":"https://openalex.org/S196647941","display_name":"IEEE Transactions on Communications","issn_l":"0090-6778","issn":["0090-6778","1558-0857"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316002","host_organization_name":"IEEE Communications Society","host_organization_lineage":["https://openalex.org/P4310316002","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Communications Society","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 Communications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://discovery.ucl.ac.uk/10164574/1/Beam_Training_and_Tracking_with_Limited_Sampling_Sets_Exploiting_Environment_Priors.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031568000","display_name":"Jianjun Zhang","orcid":"https://orcid.org/0000-0002-1404-6777"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jianjun Zhang","raw_affiliation_strings":["College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030334551","display_name":"Christos Masouros","orcid":"https://orcid.org/0000-0002-8259-6615"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Christos Masouros","raw_affiliation_strings":["Department of Electronic and Electrical Engineering, University College London, London, U.K"],"affiliations":[{"raw_affiliation_string":"Department of Electronic and Electrical Engineering, University College London, London, U.K","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056225611","display_name":"Yongming Huang","orcid":"https://orcid.org/0009-0009-2545-2875"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongming Huang","raw_affiliation_strings":["National Mobile Communications Research Laboratory, Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"National Mobile Communications Research Laboratory, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5031568000"],"corresponding_institution_ids":["https://openalex.org/I9842412"],"apc_list":null,"apc_paid":null,"fwci":0.5196,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.6245552,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"71","issue":"5","first_page":"3008","last_page":"3023"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.9998000264167786,"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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.9998000264167786,"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/T13121","display_name":"Radio Wave Propagation Studies","score":0.9506999850273132,"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/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.941100001335144,"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":0.7147024869918823},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.6856280565261841},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5918117165565491},{"id":"https://openalex.org/keywords/randomness","display_name":"Randomness","score":0.5641648173332214},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.48944297432899475},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4609113335609436},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.44587069749832153},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.4437444806098938},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.44203686714172363},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3920499384403229},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3729533851146698},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.337770938873291},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3291054964065552},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.17449364066123962},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14294859766960144},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1105959415435791}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7147024869918823},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6856280565261841},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5918117165565491},{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.5641648173332214},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.48944297432899475},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4609113335609436},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.44587069749832153},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.4437444806098938},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.44203686714172363},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3920499384403229},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3729533851146698},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.337770938873291},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3291054964065552},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.17449364066123962},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14294859766960144},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1105959415435791},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tcomm.2023.3239927","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/tcomm.2023.3239927","pdf_url":null,"source":{"id":"https://openalex.org/S196647941","display_name":"IEEE Transactions on Communications","issn_l":"0090-6778","issn":["0090-6778","1558-0857"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316002","host_organization_name":"IEEE Communications Society","host_organization_lineage":["https://openalex.org/P4310316002","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Communications Society","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 Communications","raw_type":"journal-article"},{"id":"pmh:oai:eprints.ucl.ac.uk.OAI2:10164574","is_oa":true,"landing_page_url":"https://discovery.ucl.ac.uk/id/eprint/10164574/","pdf_url":"https://discovery.ucl.ac.uk/10164574/1/Beam_Training_and_Tracking_with_Limited_Sampling_Sets_Exploiting_Environment_Priors.pdf","source":{"id":"https://openalex.org/S4306400024","display_name":"UCL Discovery (University College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45129253","host_organization_name":"University College London","host_organization_lineage":["https://openalex.org/I45129253"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"   IEEE Transactions on Communications , 71  (5)   pp. 3008-3023.   (2023)      ","raw_type":"Article"}],"best_oa_location":{"id":"pmh:oai:eprints.ucl.ac.uk.OAI2:10164574","is_oa":true,"landing_page_url":"https://discovery.ucl.ac.uk/id/eprint/10164574/","pdf_url":"https://discovery.ucl.ac.uk/10164574/1/Beam_Training_and_Tracking_with_Limited_Sampling_Sets_Exploiting_Environment_Priors.pdf","source":{"id":"https://openalex.org/S4306400024","display_name":"UCL Discovery (University College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45129253","host_organization_name":"University College London","host_organization_lineage":["https://openalex.org/I45129253"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"   IEEE Transactions on Communications , 71  (5)   pp. 3008-3023.   (2023)      ","raw_type":"Article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.41999998688697815}],"awards":[{"id":"https://openalex.org/G1158539859","display_name":null,"funder_award_id":"EP/M014126/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G4152119378","display_name":null,"funder_award_id":"61720106003","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4327584750","display_name":null,"funder_award_id":"EP/V007734/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G4585092078","display_name":null,"funder_award_id":"EP/M014150/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G5771807333","display_name":null,"funder_award_id":"EP/W026252/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G7094448879","display_name":null,"funder_award_id":"EP/S028455/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G7461758821","display_name":null,"funder_award_id":"EP/R007934/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G8224135925","display_name":"Learning to Communicate: Deep Learning based solutions for the Physical Layer of Machine Type Communications [LeanCom]","funder_award_id":"EP/S028455/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G900479562","display_name":null,"funder_award_id":"62225107","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation 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/F4320322438","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794"},{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4318141871.pdf","grobid_xml":"https://content.openalex.org/works/W4318141871.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W1870250857","https://openalex.org/W2034651337","https://openalex.org/W2116193517","https://openalex.org/W2142165404","https://openalex.org/W2195693430","https://openalex.org/W2261663070","https://openalex.org/W2406520110","https://openalex.org/W2482430271","https://openalex.org/W2555593130","https://openalex.org/W2604992528","https://openalex.org/W2614851539","https://openalex.org/W2615358882","https://openalex.org/W2725078910","https://openalex.org/W2737688805","https://openalex.org/W2754644100","https://openalex.org/W2799206304","https://openalex.org/W2805697023","https://openalex.org/W2891787690","https://openalex.org/W2947007362","https://openalex.org/W2961979974","https://openalex.org/W2962691384","https://openalex.org/W2963408914","https://openalex.org/W2963742775","https://openalex.org/W2969788250","https://openalex.org/W2972072389","https://openalex.org/W2972554461","https://openalex.org/W2974178109","https://openalex.org/W3006705564","https://openalex.org/W3016754131","https://openalex.org/W3072033075","https://openalex.org/W3097164986","https://openalex.org/W3099147043","https://openalex.org/W3108957811","https://openalex.org/W4211049957","https://openalex.org/W6684770997"],"related_works":["https://openalex.org/W3034924094","https://openalex.org/W1488708774","https://openalex.org/W3094954546","https://openalex.org/W2981906196","https://openalex.org/W4391100477","https://openalex.org/W4327779705","https://openalex.org/W1513698804","https://openalex.org/W4310560702","https://openalex.org/W2029712093","https://openalex.org/W2580650124"],"abstract_inverted_index":{"Beam":[0],"training":[1,137,197],"and":[2,25,39,66,86,107,124,199],"tracking":[3],"(BTT)":[4],"are":[5],"key":[6],"technologies":[7],"for":[8,160],"millimeter":[9],"wave":[10],"communications.":[11],"However,":[12],"since":[13],"the":[14,32,48,68,78,111,121,131,205],"effectiveness":[15],"of":[16,27,35,50,70,77,207],"BTT":[17,37,71,176],"methods":[18,128],"heavily":[19],"depends":[20],"on":[21,120,142],"wireless":[22],"environments,":[23],"complexity":[24],"randomness":[26],"practical":[28],"environments":[29],"severely":[30],"limit":[31],"application":[33],"scope":[34],"many":[36],"algorithms":[38,177,185],"even":[40,159],"invalidate":[41],"them.":[42],"To":[43],"tackle":[44],"this":[45,55],"issue,":[46],"from":[47],"perspective":[49],"stochastic":[51],"process":[52,73,113],"(SP),":[53],"in":[54,167,172],"paper":[56],"we":[57],"propose":[58,125],"to":[59,95,101,118,129,174],"model":[60],"beam":[61,136,189],"directions":[62],"as":[63,115],"a":[64,181,193],"SP":[65,79,99,148],"address":[67],"problem":[69],"via":[72],"inference.":[74],"The":[75,146],"benefit":[76],"design":[80,122],"methodology":[81,123,149],"is":[82,139,164],"that":[83,178],"environment":[84],"priors":[85],"uncertainties":[87],"can":[88,156],"be":[89,157],"naturally":[90],"taken":[91],"into":[92,98],"account":[93],"(e.g.,":[94,105],"encode":[96],"them":[97],"distribution)":[100],"improve":[102],"prediction":[103,132],"efficiencies":[104],"accuracy":[106],"robustness).":[108],"We":[109],"take":[110],"Gaussian":[112],"(GP)":[114],"an":[116,187],"example":[117],"elaborate":[119],"novel":[126],"learning":[127],"optimize":[130],"models.":[133],"In":[134],"particular,":[135],"subset":[138],"optimized":[140],"based":[141],"derived":[143],"posterior":[144],"distribution.":[145],"GP-based":[147],"enjoys":[150],"two":[151],"advantages.":[152],"First,":[153],"good":[154],"performance":[155],"achieved":[158],"small":[161],"data,":[162],"which":[163,191],"very":[165],"appealing":[166],"dynamic":[168],"communication":[169],"scenarios.":[170],"Second,":[171],"contrast":[173],"most":[175],"only":[179],"predict":[180],"single":[182],"beam,":[183],"our":[184,208],"output":[186],"optimizable":[188],"subset,":[190],"enables":[192],"flexible":[194],"tradeoff":[195],"between":[196],"overhead":[198],"desired":[200],"performance.":[201],"Simulation":[202],"results":[203],"show":[204],"superiority":[206],"approach.":[209]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
