{"id":"https://openalex.org/W7140584665","doi":"https://doi.org/10.1109/ieeeconf67917.2025.11443708","title":"Gridless Variational Bayesian DoA Estimation with Few-Bit ADCs","display_name":"Gridless Variational Bayesian DoA Estimation with Few-Bit ADCs","publication_year":2025,"publication_date":"2025-10-26","ids":{"openalex":"https://openalex.org/W7140584665","doi":"https://doi.org/10.1109/ieeeconf67917.2025.11443708"},"language":null,"primary_location":{"id":"doi:10.1109/ieeeconf67917.2025.11443708","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf67917.2025.11443708","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 59th Asilomar Conference on Signals, Systems, and Computers","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/A5021007706","display_name":"Toan-Van Nguyen","orcid":"https://orcid.org/0000-0002-1968-8170"},"institutions":[{"id":"https://openalex.org/I26538001","display_name":"San Diego State University","ror":"https://ror.org/0264fdx42","country_code":"US","type":"education","lineage":["https://openalex.org/I26538001"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Toan-Van Nguyen","raw_affiliation_strings":["San Diego State University,Department of Electrical and Computer Engineering,San Diego,CA,USA"],"affiliations":[{"raw_affiliation_string":"San Diego State University,Department of Electrical and Computer Engineering,San Diego,CA,USA","institution_ids":["https://openalex.org/I26538001"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130678406","display_name":"A. Lee Swindlehurst","orcid":null},"institutions":[{"id":"https://openalex.org/I204250578","display_name":"University of California, Irvine","ror":"https://ror.org/04gyf1771","country_code":"US","type":"education","lineage":["https://openalex.org/I204250578"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"A. Lee Swindlehurst","raw_affiliation_strings":["University of California,Department of Electrical Engineering and Computer Science,Irvine,CA,USA"],"affiliations":[{"raw_affiliation_string":"University of California,Department of Electrical Engineering and Computer Science,Irvine,CA,USA","institution_ids":["https://openalex.org/I204250578"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100986204","display_name":"Duy H. N Nguyen","orcid":null},"institutions":[{"id":"https://openalex.org/I26538001","display_name":"San Diego State University","ror":"https://ror.org/0264fdx42","country_code":"US","type":"education","lineage":["https://openalex.org/I26538001"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Duy H. N. Nguyen","raw_affiliation_strings":["San Diego State University,Department of Electrical and Computer Engineering,San Diego,CA,USA"],"affiliations":[{"raw_affiliation_string":"San Diego State University,Department of Electrical and Computer Engineering,San Diego,CA,USA","institution_ids":["https://openalex.org/I26538001"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5021007706"],"corresponding_institution_ids":["https://openalex.org/I26538001"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.62711843,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1238","last_page":"1242"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10323","display_name":"Analog and Mixed-Signal Circuit Design","score":0.7343999743461609,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10323","display_name":"Analog and Mixed-Signal Circuit Design","score":0.7343999743461609,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.09319999814033508,"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/T10363","display_name":"Low-power high-performance VLSI design","score":0.015399999916553497,"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/bayesian-probability","display_name":"Bayesian probability","score":0.539900004863739},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.33480000495910645},{"id":"https://openalex.org/keywords/bayes-estimator","display_name":"Bayes estimator","score":0.3264999985694885},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.30559998750686646},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.2953999936580658},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.2921999990940094}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5652999877929688},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.539900004863739},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5113000273704529},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36899998784065247},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.33480000495910645},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.3264999985694885},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3077999949455261},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.30559998750686646},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2953999936580658},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.2921999990940094},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.287200003862381},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2759999930858612},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.2630000114440918}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ieeeconf67917.2025.11443708","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf67917.2025.11443708","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 59th Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41979554295539856,"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320316514","display_name":"Arm","ror":"https://ror.org/04mmhzs81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2109958193","https://openalex.org/W2141682101","https://openalex.org/W2159448753","https://openalex.org/W2339230339","https://openalex.org/W2528695050","https://openalex.org/W2800315826","https://openalex.org/W2909890640","https://openalex.org/W2990352483","https://openalex.org/W3104690928","https://openalex.org/W3210343664","https://openalex.org/W4285219251","https://openalex.org/W4317795053","https://openalex.org/W4400645074","https://openalex.org/W4409156575"],"related_works":[],"abstract_inverted_index":{"Efficient":[0],"direction":[1],"of":[2,47,51,83,101],"arrival":[3],"(DoA)":[4],"estimation":[5,27,75,95],"is":[6],"vital":[7],"in":[8,81],"radar,":[9],"wireless":[10],"communication,":[11],"and":[12,15,55,77,93],"integrated":[13],"sensing":[14],"communication":[16],"systems.":[17],"This":[18],"paper":[19],"develops":[20],"a":[21,98],"novel":[22],"gridless-VB":[23,66],"algorithm":[24,67],"for":[25],"DoA":[26],"from":[28],"quantized":[29],"measurements":[30],"obtained":[31],"via":[32],"few-bit":[33],"analog-to-digital":[34],"converters":[35],"(ADCs).":[36],"Our":[37],"approach":[38],"leverages":[39],"the":[40,48,52,64],"VB":[41],"framework":[42],"to":[43],"derive":[44],"variational":[45,72],"distributions":[46],"spatial":[49],"frequencies":[50],"arriving":[53],"paths":[54],"their":[56],"corresponding":[57],"path":[58],"gains.":[59],"Simulation":[60],"results":[61],"show":[62],"that":[63],"proposed":[65],"outperforms":[68],"multiple":[69],"signal":[70],"classification,":[71],"line":[73],"spectral":[74],"(VALSE),":[76],"VALSE-expectation":[78],"propagation":[79],"algorithms":[80],"terms":[82],"normalized":[84],"mean-squared":[85],"error":[86],"(NMSE),":[87],"as":[88,90],"well":[89],"frequency,":[91],"gain,":[92],"phase":[94],"accuracy":[96],"across":[97],"wide":[99],"range":[100],"SNRs.":[102]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-03-27T00:00:00"}
