{"id":"https://openalex.org/W2160200397","doi":"https://doi.org/10.1109/allerton.2010.5706948","title":"Markov Chain Monte Carlo detection over time-varying frequency-selective channels","display_name":"Markov Chain Monte Carlo detection over time-varying frequency-selective channels","publication_year":2010,"publication_date":"2010-09-01","ids":{"openalex":"https://openalex.org/W2160200397","doi":"https://doi.org/10.1109/allerton.2010.5706948","mag":"2160200397"},"language":"en","primary_location":{"id":"doi:10.1109/allerton.2010.5706948","is_oa":false,"landing_page_url":"https://doi.org/10.1109/allerton.2010.5706948","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","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/A5109965190","display_name":"Hong Wan","orcid":null},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hong Wan","raw_affiliation_strings":["University of Utah, USA"],"affiliations":[{"raw_affiliation_string":"University of Utah, USA","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100734558","display_name":"Rong\u2010Rong Chen","orcid":"https://orcid.org/0009-0002-2955-9652"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rong-Rong Chen","raw_affiliation_strings":["University of Utah, USA"],"affiliations":[{"raw_affiliation_string":"University of Utah, USA","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102839991","display_name":"Jun Won Choi","orcid":"https://orcid.org/0000-0002-3733-0148"},"institutions":[{"id":"https://openalex.org/I4210111675","display_name":"Market Matters","ror":"https://ror.org/021yan307","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210111675"]},{"id":"https://openalex.org/I4210087596","display_name":"Qualcomm (United States)","ror":"https://ror.org/002zrf773","country_code":"US","type":"company","lineage":["https://openalex.org/I4210087596"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jun Won Choi","raw_affiliation_strings":["Qualcomm Inc., USA"],"affiliations":[{"raw_affiliation_string":"Qualcomm Inc., USA","institution_ids":["https://openalex.org/I4210087596","https://openalex.org/I4210111675"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021743930","display_name":"James C. Preisig","orcid":"https://orcid.org/0000-0002-7066-8474"},"institutions":[{"id":"https://openalex.org/I66958751","display_name":"Woods Hole Oceanographic Institution","ror":"https://ror.org/03zbnzt98","country_code":"US","type":"funder","lineage":["https://openalex.org/I66958751"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James Preisig","raw_affiliation_strings":["Woods Hole Oceanographic Institution, USA"],"affiliations":[{"raw_affiliation_string":"Woods Hole Oceanographic Institution, USA","institution_ids":["https://openalex.org/I66958751"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056002282","display_name":"Behrouz Farhang\u2010Boroujeny","orcid":"https://orcid.org/0000-0003-3008-6725"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Behrouz Farhang-Boroujeny","raw_affiliation_strings":["University of Utah, USA"],"affiliations":[{"raw_affiliation_string":"University of Utah, USA","institution_ids":["https://openalex.org/I223532165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5109965190"],"corresponding_institution_ids":["https://openalex.org/I223532165"],"apc_list":null,"apc_paid":null,"fwci":0.6453,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.76148376,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"502","last_page":"506"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11192","display_name":"Underwater Vehicles and Communication Systems","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11192","display_name":"Underwater Vehicles and Communication Systems","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11698","display_name":"Underwater Acoustics Research","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9962000250816345,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.8560104370117188},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6899965405464172},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.6739012598991394},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6527969241142273},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.6091892719268799},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.5063525438308716},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.43277424573898315},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.41174110770225525},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.2561523914337158},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.25245654582977295},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2332494556903839},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.21258795261383057},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.15250727534294128},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.06672871112823486}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.8560104370117188},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6899965405464172},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.6739012598991394},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6527969241142273},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6091892719268799},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5063525438308716},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.43277424573898315},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.41174110770225525},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2561523914337158},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.25245654582977295},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2332494556903839},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.21258795261383057},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.15250727534294128},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.06672871112823486}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/allerton.2010.5706948","is_oa":false,"landing_page_url":"https://doi.org/10.1109/allerton.2010.5706948","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/14","display_name":"Life below water","score":0.44999998807907104}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1546756991","https://openalex.org/W2020082283","https://openalex.org/W2100132363","https://openalex.org/W2101617851","https://openalex.org/W2105324718","https://openalex.org/W2107959360","https://openalex.org/W2119883400","https://openalex.org/W2123299872","https://openalex.org/W2129583471","https://openalex.org/W2138722795","https://openalex.org/W2141191255","https://openalex.org/W2153555078","https://openalex.org/W2156511215"],"related_works":["https://openalex.org/W3125971950","https://openalex.org/W1580681286","https://openalex.org/W2160822965","https://openalex.org/W1984743304","https://openalex.org/W2116700007","https://openalex.org/W2175355783","https://openalex.org/W1579866848","https://openalex.org/W3139342328","https://openalex.org/W2622204791","https://openalex.org/W1546022168"],"abstract_inverted_index":{"In":[0],"this":[1,75],"paper,":[2],"we":[3],"develop":[4],"a":[5,47,113,121,126],"novel":[6],"list":[7,48],"channel":[8,27,36,58,94,98,108],"refinement":[9],"(LCR)":[10],"based":[11,73,83],"Markov":[12],"Chain":[13],"Monte":[14],"Carlo":[15],"(MCMC)":[16],"detector":[17,40],"for":[18,31],"time-varying":[19],"frequency-selective":[20],"channels.":[21],"Information":[22],"fed":[23],"back":[24],"from":[25,152],"the":[26,65,70,78,85,118,140],"decoder":[28],"is":[29,81,100,130,142],"exploited":[30],"both":[32,145],"data":[33,91,103,150],"detection":[34,92,124],"and":[35,93,129,148],"estimation.":[37,95],"The":[38,110],"LCR-MCMC":[39,111,141],"adopts":[41],"parallel":[42],"Gibbs":[43],"samplers":[44],"to":[45,87,105,116,132],"find":[46],"of":[49,69,139],"mostly":[50],"likely":[51],"transmitted":[52,71],"sequences":[53],"as":[54,56],"well":[55],"matching":[57],"impulse":[59],"response":[60],"(CIR).":[61],"It":[62],"then":[63],"computes":[64],"log-likelihood":[66],"ratio":[67],"(LLR)":[68],"bits":[72],"on":[74,84],"list.":[76],"Furthermore,":[77],"estimated":[79],"CIR":[80],"refined":[82],"LLRs":[86],"facilitate":[88],"iterative":[89],"joint":[90],"A":[96],"bidirectional":[97],"estimation":[99],"performed":[101],"among":[102],"segments":[104],"provide":[106],"initial":[107],"estimates.":[109],"provides":[112],"low-complexity":[114],"means":[115],"approximate":[117],"optimal":[119],"maximum":[120],"posterior":[122],"(MAP)":[123],"in":[125],"statistical":[127],"fashion":[128],"applicable":[131],"channels":[133,147],"with":[134],"long":[135],"memory.":[136],"Excellent":[137],"behavior":[138],"presented":[143],"using":[144],"synthetic":[146],"real":[149],"collected":[151],"actual":[153],"underwater":[154],"acoustic":[155],"experiments.":[156]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
