{"id":"https://openalex.org/W4200274211","doi":"https://doi.org/10.1109/wcsp52459.2021.9613650","title":"Joint Estimation of DOA and Channel Response Aided by Magnitude Measurements","display_name":"Joint Estimation of DOA and Channel Response Aided by Magnitude Measurements","publication_year":2021,"publication_date":"2021-10-20","ids":{"openalex":"https://openalex.org/W4200274211","doi":"https://doi.org/10.1109/wcsp52459.2021.9613650"},"language":"en","primary_location":{"id":"doi:10.1109/wcsp52459.2021.9613650","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcsp52459.2021.9613650","pdf_url":null,"source":{"id":"https://openalex.org/S4363607893","display_name":"2021 13th International Conference on Wireless Communications and Signal Processing (WCSP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 13th International Conference on Wireless Communications and Signal Processing (WCSP)","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/A5078684451","display_name":"Ke Nie","orcid":"https://orcid.org/0000-0002-7774-2200"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ke Nie","raw_affiliation_strings":["Beijing University of Posts and Communications (BUPT), Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Communications (BUPT), Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056511292","display_name":"Shengchu Wang","orcid":"https://orcid.org/0000-0003-0876-8842"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengchu Wang","raw_affiliation_strings":["Beijing University of Posts and Communications (BUPT), Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Communications (BUPT), Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5078684451"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.5161,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.65681666,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9998000264167786,"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/T11946","display_name":"Antenna Design and Optimization","score":0.9979000091552734,"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/T10860","display_name":"Speech and Audio Processing","score":0.9940999746322632,"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":0.7868751287460327},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7524524927139282},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6029064655303955},{"id":"https://openalex.org/keywords/magnitude","display_name":"Magnitude (astronomy)","score":0.591202437877655},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5403459668159485},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.5274325013160706},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5115984082221985},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.5110936760902405},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.5037931799888611},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.461215615272522},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4473520815372467},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.40636080503463745},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1889708936214447},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.18288937211036682},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14393764734268188},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.12240034341812134}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.7868751287460327},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7524524927139282},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6029064655303955},{"id":"https://openalex.org/C126691448","wikidata":"https://www.wikidata.org/wiki/Q2028919","display_name":"Magnitude (astronomy)","level":2,"score":0.591202437877655},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5403459668159485},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.5274325013160706},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5115984082221985},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5110936760902405},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.5037931799888611},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.461215615272522},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4473520815372467},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40636080503463745},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1889708936214447},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.18288937211036682},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14393764734268188},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.12240034341812134},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","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/wcsp52459.2021.9613650","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcsp52459.2021.9613650","pdf_url":null,"source":{"id":"https://openalex.org/S4363607893","display_name":"2021 13th International Conference on Wireless Communications and Signal Processing (WCSP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 13th International Conference on Wireless Communications and Signal Processing (WCSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8799999952316284}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1491306579","https://openalex.org/W1831233010","https://openalex.org/W2001613283","https://openalex.org/W2004294022","https://openalex.org/W2033031309","https://openalex.org/W2128131274","https://openalex.org/W2419766254","https://openalex.org/W2480106214","https://openalex.org/W2685695880","https://openalex.org/W2761315682","https://openalex.org/W2796666321","https://openalex.org/W2799081448","https://openalex.org/W3031418291","https://openalex.org/W3111537414","https://openalex.org/W3127783460","https://openalex.org/W3137677720","https://openalex.org/W6629244292"],"related_works":["https://openalex.org/W71678127","https://openalex.org/W2157655363","https://openalex.org/W4205763938","https://openalex.org/W2292189132","https://openalex.org/W4288092343","https://openalex.org/W4386114318","https://openalex.org/W2134332527","https://openalex.org/W2888496681","https://openalex.org/W2790979771","https://openalex.org/W4289670352"],"abstract_inverted_index":{"This":[0],"paper":[1],"discusses":[2],"the":[3,28,37,46,59,74,86,112,116,119,160,175,183],"joint":[4,47],"estimation":[5],"of":[6,39,53,93,122],"direction-of-arrivals":[7],"(DOAs)":[8],"and":[9,43,55,67,89,95,124,132,136,146,167,171],"channel":[10,56,96,125,168],"responses":[11,57,97,126],"in":[12],"a":[13],"novel":[14],"magnitude-aided":[15],"antenna":[16],"array":[17],"(MA-AA),":[18],"where":[19],"magnitude-only":[20],"radio":[21],"frequency":[22],"(RF)":[23],"chains":[24],"are":[25,108,127,139],"introduced":[26],"into":[27],"classical":[29],"AA":[30],"to":[31,36,84],"acquire":[32],"magnitude":[33,41,87],"observations.":[34],"Due":[35],"existence":[38],"nonlinear":[40],"measurements":[42,62],"high-dimensional":[44],"integration,":[45],"posterior":[48,71,100,106],"probability":[49],"distribution":[50],"functions":[51],"(pdfs)":[52],"DOAs":[54,94,123],"over":[58],"hybrid":[60],"MA-AA":[61,178],"have":[63],"no":[64],"explicit":[65],"expression,":[66],"neither":[68],"do":[69],"their":[70,99,105,137],"means.":[72],"Consequently,":[73],"Metropolis-Hastings":[75],"Markov":[76],"chain":[77],"Monte":[78],"Carlo":[79],"(MH-MCMC)":[80],"algorithm":[81],"is":[82,179],"modified":[83,117,161],"handle":[85],"measurements,":[88],"then":[90],"generate":[91],"samples":[92],"obeying":[98],"pdfs,":[101,135],"based":[102,150],"on":[103,151,165],"which,":[104],"means":[107],"numerically":[109],"calculated":[110],"after":[111],"burn-in":[113],"period.":[114],"In":[115],"MH-MCMC,":[118],"independent":[120],"proposals":[121],"designed":[128],"as":[129],"uniform":[130],"pdfs":[131],"complex":[133,153],"Gaussian":[134],"hyper-parameters":[138],"obtained":[140],"by":[141],"multiple":[142],"signal":[143],"classification":[144],"(MUSIC)":[145],"least":[147],"square":[148],"(LS)":[149],"several":[152],"observations,":[154],"respectively.":[155],"Compared":[156],"with":[157],"existing":[158],"estimators,":[159],"MH-MCMC":[162,176],"shows":[163],"superiorities":[164],"DOA":[166],"response":[169],"estimation,":[170],"computational":[172],"complexity.":[173],"With":[174],"estimator,":[177],"more":[180],"energy-efficient":[181],"than":[182],"conventional":[184],"AA.":[185]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
