{"id":"https://openalex.org/W4410229365","doi":"https://doi.org/10.1109/wcnc61545.2025.10978699","title":"MCMC-Based Sparse Bayesian Learning for Channel Estimation Using Gaussian Mixture Models","display_name":"MCMC-Based Sparse Bayesian Learning for Channel Estimation Using Gaussian Mixture Models","publication_year":2025,"publication_date":"2025-03-24","ids":{"openalex":"https://openalex.org/W4410229365","doi":"https://doi.org/10.1109/wcnc61545.2025.10978699"},"language":"en","primary_location":{"id":"doi:10.1109/wcnc61545.2025.10978699","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc61545.2025.10978699","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Wireless Communications and Networking Conference (WCNC)","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/A5109479191","display_name":"Xiaotian Fan","orcid":null},"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":"Xiaotian Fan","raw_affiliation_strings":["Southeast University,National Mobile Communications Research Laboratory,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southeast University,National Mobile Communications Research Laboratory,Nanjing,China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024019526","display_name":"Xingyu Zhou","orcid":"https://orcid.org/0000-0002-4439-7658"},"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":"Xingyu Zhou","raw_affiliation_strings":["Southeast University,National Mobile Communications Research Laboratory,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southeast University,National Mobile Communications Research Laboratory,Nanjing,China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100951262","display_name":"Hao Ye","orcid":"https://orcid.org/0009-0004-0535-207X"},"institutions":[{"id":"https://openalex.org/I185103710","display_name":"University of California, Santa Cruz","ror":"https://ror.org/03s65by71","country_code":"US","type":"education","lineage":["https://openalex.org/I185103710"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hao Ye","raw_affiliation_strings":["University of California,Department of Electrical and Computer Engineering,Santa Cruz,CA,95064"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California,Department of Electrical and Computer Engineering,Santa Cruz,CA,95064","institution_ids":["https://openalex.org/I185103710"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066151952","display_name":"Le Liang","orcid":"https://orcid.org/0000-0002-8489-1933"},"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":"Le Liang","raw_affiliation_strings":["Southeast University,National Mobile Communications Research Laboratory,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southeast University,National Mobile Communications Research Laboratory,Nanjing,China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013079905","display_name":"Shi Jin","orcid":"https://orcid.org/0000-0003-0271-6021"},"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":"Shi Jin","raw_affiliation_strings":["Southeast University,National Mobile Communications Research Laboratory,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southeast University,National Mobile Communications Research Laboratory,Nanjing,China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10860","display_name":"Speech and Audio Processing","score":0.9635000228881836,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9506000280380249,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/computer-science","display_name":"Computer science","score":0.6413480043411255},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.6402875185012817},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5811489820480347},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5740979313850403},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5515753626823425},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4704034924507141},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.44612330198287964},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4367644488811493},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43494173884391785},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43430429697036743},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.05986428260803223}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6413480043411255},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.6402875185012817},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5811489820480347},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5740979313850403},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5515753626823425},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4704034924507141},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.44612330198287964},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4367644488811493},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43494173884391785},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43430429697036743},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.05986428260803223},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wcnc61545.2025.10978699","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc61545.2025.10978699","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Wireless Communications and Networking Conference (WCNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8133500138","display_name":null,"funder_award_id":"62201145,62231019,623B2019,62261160576","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8517709173","display_name":null,"funder_award_id":"BE2023022,BE2023022-1","funder_id":"https://openalex.org/F4320327777","funder_display_name":"Jiangsu Provincial Key Research and Development Program"},{"id":"https://openalex.org/G8585003821","display_name":null,"funder_award_id":"2242022K60004,2242023K5003","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327777","display_name":"Jiangsu Provincial Key Research and Development Program","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2026933032","https://openalex.org/W2120549790","https://openalex.org/W2122409554","https://openalex.org/W2141682101","https://openalex.org/W2147035723","https://openalex.org/W2148154358","https://openalex.org/W2339667469","https://openalex.org/W2612354068","https://openalex.org/W2756105777","https://openalex.org/W2920485214","https://openalex.org/W3187410184","https://openalex.org/W4390017968","https://openalex.org/W4392902747","https://openalex.org/W4401871087"],"related_works":["https://openalex.org/W1578916557","https://openalex.org/W4283077537","https://openalex.org/W2999603699","https://openalex.org/W2464065341","https://openalex.org/W2947536360","https://openalex.org/W2968689489","https://openalex.org/W4302573481","https://openalex.org/W2505308168","https://openalex.org/W2963960970","https://openalex.org/W2407375987"],"abstract_inverted_index":{"This":[0],"paper":[1],"investigates":[2],"the":[3,24,43,46,51,61,90,93,100],"downlink":[4],"channel":[5,47,94,103],"estimation":[6,104],"problem":[7,22],"for":[8],"frequency":[9],"division":[10],"duplex":[11],"(FDD)":[12],"multi-user":[13,114],"massive":[14],"multiple-input":[15],"multiple-output":[16],"(MIMO)":[17],"systems.":[18],"We":[19],"model":[20],"this":[21],"within":[23],"sparse":[25],"Bayesian":[26,81],"learning":[27],"(SBL)":[28],"framework,":[29],"where":[30],"all":[31,86],"unknowns":[32],"are":[33],"treated":[34],"as":[35],"random":[36,87],"variables.":[37],"Due":[38],"to":[39,59,79],"limited":[40],"scattering":[41],"at":[42],"base":[44],"station,":[45],"exhibits":[48],"sparsity":[49,65],"in":[50,89,113],"angular":[52],"domain.":[53],"By":[54],"introducing":[55],"Gaussian":[56],"mixture":[57],"priors":[58],"characterize":[60],"user":[62],"equipment":[63],"internal":[64],"and":[66,83],"partially":[67],"shared":[68],"sparsity,":[69],"we":[70],"develop":[71],"a":[72],"Markov":[73],"chain":[74],"Monte":[75],"Carlo":[76],"(MCMC)":[77],"method":[78],"implement":[80],"inference":[82],"accurately":[84],"estimate":[85],"variables":[88],"model,":[91],"including":[92],"matrix.":[95],"Experimental":[96],"results":[97],"demonstrate":[98],"that":[99],"MCMC-based":[101],"SBL":[102],"algorithm":[105],"outperforms":[106],"existing":[107],"approaches":[108],"by":[109],"over":[110],"5":[111],"dB":[112],"scenarios":[115],"while":[116],"reducing":[117],"pilot":[118],"overhead.":[119]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-17T09:13:05.818461","created_date":"2025-10-10T00:00:00"}
