{"id":"https://openalex.org/W4297882658","doi":"https://doi.org/10.1145/3492866.3549732","title":"Sampling of the wiener process for remote estimation over a channel with unknown delay statistics","display_name":"Sampling of the wiener process for remote estimation over a channel with unknown delay statistics","publication_year":2022,"publication_date":"2022-09-21","ids":{"openalex":"https://openalex.org/W4297882658","doi":"https://doi.org/10.1145/3492866.3549732"},"language":"en","primary_location":{"id":"doi:10.1145/3492866.3549732","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3492866.3549732","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3492866.3549732","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3492866.3549732","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021894541","display_name":"Haoyue Tang","orcid":"https://orcid.org/0000-0003-1227-8715"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Haoyue Tang","raw_affiliation_strings":["Yale University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003200620","display_name":"Yin Sun","orcid":"https://orcid.org/0000-0001-6811-984X"},"institutions":[{"id":"https://openalex.org/I82497590","display_name":"Auburn University","ror":"https://ror.org/02v80fc35","country_code":"US","type":"education","lineage":["https://openalex.org/I82497590"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yin Sun","raw_affiliation_strings":["Auburn University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Auburn University","institution_ids":["https://openalex.org/I82497590"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014892027","display_name":"Leandros Tassiulas","orcid":"https://orcid.org/0000-0003-0932-774X"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Leandros Tassiulas","raw_affiliation_strings":["Yale University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5021894541"],"corresponding_institution_ids":["https://openalex.org/I32971472"],"apc_list":null,"apc_paid":null,"fwci":1.2826,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.81201765,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"51","last_page":"60"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13553","display_name":"Age of Information Optimization","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T13553","display_name":"Age of Information Optimization","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10579","display_name":"Cognitive Radio Networks and Spectrum Sensing","score":0.9750999808311462,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/estimator","display_name":"Estimator","score":0.6969301104545593},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6753203868865967},{"id":"https://openalex.org/keywords/regret","display_name":"Regret","score":0.5647537708282471},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5587046146392822},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5236596465110779},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4813709855079651},{"id":"https://openalex.org/keywords/wiener-process","display_name":"Wiener process","score":0.4448779821395874},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4435444176197052},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.43655848503112793},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3955771327018738},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.37873169779777527},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.09828943014144897}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6969301104545593},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6753203868865967},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.5647537708282471},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5587046146392822},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5236596465110779},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4813709855079651},{"id":"https://openalex.org/C60391097","wikidata":"https://www.wikidata.org/wiki/Q1056809","display_name":"Wiener process","level":2,"score":0.4448779821395874},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4435444176197052},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.43655848503112793},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3955771327018738},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.37873169779777527},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.09828943014144897},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3492866.3549732","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3492866.3549732","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3492866.3549732","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3492866.3549732","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3492866.3549732","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3492866.3549732","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1432373144","display_name":null,"funder_award_id":"W911NF-21-1-","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G1740375318","display_name":"CIF: Small: Collaborative Research: On the Fundamental Nature of the Age of Updates","funder_award_id":"1813078","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2245803972","display_name":null,"funder_award_id":"CNS-2112562","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G3455827733","display_name":null,"funder_award_id":"W911NF-21-1-0244","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G4244981204","display_name":null,"funder_award_id":"N00014-19-1-2566","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G7452299184","display_name":null,"funder_award_id":"W911NF","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G7709164144","display_name":null,"funder_award_id":"CNS-2112562","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8377316191","display_name":null,"funder_award_id":"2112562","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8876996369","display_name":null,"funder_award_id":"N00014","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4297882658.pdf","grobid_xml":"https://content.openalex.org/works/W4297882658.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W1511694993","https://openalex.org/W1640967807","https://openalex.org/W1993918491","https://openalex.org/W1994616650","https://openalex.org/W1995643834","https://openalex.org/W2496316373","https://openalex.org/W2546584265","https://openalex.org/W2744248483","https://openalex.org/W2964201338","https://openalex.org/W2970983057","https://openalex.org/W2974930049","https://openalex.org/W2976631814","https://openalex.org/W3020925646","https://openalex.org/W3133603318","https://openalex.org/W3138649231","https://openalex.org/W3164366798","https://openalex.org/W3173628078","https://openalex.org/W3200517331","https://openalex.org/W4226058285","https://openalex.org/W4236881602","https://openalex.org/W4244787982","https://openalex.org/W4286233898"],"related_works":["https://openalex.org/W2971351794","https://openalex.org/W4376155396","https://openalex.org/W1947085858","https://openalex.org/W2174986909","https://openalex.org/W2527791220","https://openalex.org/W2101991911","https://openalex.org/W3125536267","https://openalex.org/W2274645452","https://openalex.org/W1999706086","https://openalex.org/W2521753262"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,51,93,123],"study":[4],"an":[5,53],"online":[6,54,103,116],"sampling":[7,29,40,55,63,104,117],"problem":[8,41],"of":[9,23,85,101,127],"the":[10,18,24,33,61,71,83,96,114,125,128],"Wiener":[11],"process.":[12],"The":[13,39],"goal":[14],"is":[15,37,42,82,106,119],"to":[16],"minimize":[17],"mean":[19],"squared":[20],"error":[21],"(MSE)":[22],"remote":[25],"estimator":[26],"under":[27],"a":[28,45],"frequency":[30],"constraint":[31],"when":[32],"transmission":[34],"delay":[35],"distribution":[36],"unknown.":[38],"reformulated":[43],"into":[44],"renewal":[46],"reward":[47],"optimization":[48],"problem,":[49],"and":[50],"propose":[52],"algorithm":[56,105,118,130],"that":[57,70,95],"can":[58],"adaptively":[59],"learn":[60],"optimal":[62],"policy":[64],"through":[65],"stochastic":[66],"approximation.":[67],"We":[68],"show":[69,94],"cumulative":[72,98],"MSE":[73,99],"regret":[74,100],"grows":[75],"with":[76],"rate":[77],"O(ln":[78],"k),":[79],"where":[80],"k":[81],"number":[84],"samples.":[86],"Through":[87],"Le":[88],"Cam's":[89],"two":[90],"point":[91],"method,":[92],"worst-case":[97],"any":[102],"lower":[107],"bounded":[108],"by":[109],"\u03a9":[110],"(ln":[111],"k).":[112],"Hence,":[113],"proposed":[115,129],"minimax":[120],"order-optimal.":[121],"Finally,":[122],"validate":[124],"performance":[126],"via":[131],"numerical":[132],"simulations.":[133]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":6}],"updated_date":"2026-05-09T13:55:54.758798","created_date":"2025-10-10T00:00:00"}