{"id":"https://openalex.org/W2402526535","doi":"https://doi.org/10.1109/icassp.2016.7472309","title":"Millimeter wave communications channel estimation via Bayesian group sparse recovery","display_name":"Millimeter wave communications channel estimation via Bayesian group sparse recovery","publication_year":2016,"publication_date":"2016-03-01","ids":{"openalex":"https://openalex.org/W2402526535","doi":"https://doi.org/10.1109/icassp.2016.7472309","mag":"2402526535"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2016.7472309","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7472309","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5070083867","display_name":"Raj Tejas Suryaprakash","orcid":null},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Raj Tejas Suryaprakash","raw_affiliation_strings":["Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042113276","display_name":"Milutin Pajovic","orcid":"https://orcid.org/0000-0001-5033-017X"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Milutin Pajovic","raw_affiliation_strings":["Mitsubishi Electric Research Labs, Cambridge, MA"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Labs, Cambridge, MA","institution_ids":["https://openalex.org/I4210159266"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059938882","display_name":"Kyeong Jin Kim","orcid":"https://orcid.org/0000-0002-1943-8307"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kyeong Jin Kim","raw_affiliation_strings":["Mitsubishi Electric Research Labs, Cambridge, MA"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Labs, Cambridge, MA","institution_ids":["https://openalex.org/I4210159266"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085881975","display_name":"Phil Orlik","orcid":null},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Phil Orlik","raw_affiliation_strings":["Mitsubishi Electric Research Labs, Cambridge, MA"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Labs, Cambridge, MA","institution_ids":["https://openalex.org/I4210159266"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5070083867"],"corresponding_institution_ids":["https://openalex.org/I27837315"],"apc_list":null,"apc_paid":null,"fwci":3.0396,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.90180741,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"53","issue":null,"first_page":"3406","last_page":"3410"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9973999857902527,"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/T10860","display_name":"Speech and Audio Processing","score":0.9970999956130981,"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/channel","display_name":"Channel (broadcasting)","score":0.7561138868331909},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6859913468360901},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.6302232146263123},{"id":"https://openalex.org/keywords/extremely-high-frequency","display_name":"Extremely high frequency","score":0.5792797803878784},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.5244964957237244},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5049312710762024},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.503302276134491},{"id":"https://openalex.org/keywords/millimeter","display_name":"Millimeter","score":0.44193124771118164},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.36506354808807373},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.28331682085990906},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24461618065834045},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1299932599067688},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.11936649680137634},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.09366250038146973}],"concepts":[{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.7561138868331909},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6859913468360901},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.6302232146263123},{"id":"https://openalex.org/C45764600","wikidata":"https://www.wikidata.org/wiki/Q570342","display_name":"Extremely high frequency","level":2,"score":0.5792797803878784},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.5244964957237244},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5049312710762024},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.503302276134491},{"id":"https://openalex.org/C109792285","wikidata":"https://www.wikidata.org/wiki/Q174789","display_name":"Millimeter","level":2,"score":0.44193124771118164},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.36506354808807373},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.28331682085990906},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24461618065834045},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1299932599067688},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.11936649680137634},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.09366250038146973},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2016.7472309","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7472309","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W905396345","https://openalex.org/W1514337951","https://openalex.org/W1921359321","https://openalex.org/W1931505599","https://openalex.org/W2017607348","https://openalex.org/W2031839259","https://openalex.org/W2100526560","https://openalex.org/W2123457453","https://openalex.org/W2127271355","https://openalex.org/W2140856955","https://openalex.org/W2170844819"],"related_works":["https://openalex.org/W2317169686","https://openalex.org/W1185324648","https://openalex.org/W2539553997","https://openalex.org/W3159902002","https://openalex.org/W3208155863","https://openalex.org/W2363237216","https://openalex.org/W2033762247","https://openalex.org/W2530392398","https://openalex.org/W2070366004","https://openalex.org/W1997983170"],"abstract_inverted_index":{"We":[0,12,38,75],"consider":[1],"the":[2,25,35,72,77,91],"problem":[3],"of":[4,34,62,68],"channel":[5,14,36],"estimation":[6,15,87],"for":[7,50],"millimeter":[8],"wave":[9],"communications":[10],"(mmWave).":[11],"formulate":[13],"as":[16,58],"a":[17,31,40],"structured":[18],"sparse":[19,43],"signal":[20,26],"recovery":[21,44],"problem,":[22],"in":[23],"which":[24,46],"structure":[27],"is":[28],"governed":[29],"by":[30],"priori":[32],"knowledge":[33],"characteristics.":[37],"develop":[39],"Bayesian":[41],"group":[42],"algorithm":[45],"takes":[47],"into":[48],"account":[49],"several":[51],"features":[52],"unique":[53],"to":[54,90],"mmWave":[55],"channels,":[56],"such":[57],"spatial":[59],"(angular)":[60],"spreads":[61],"received":[63],"signals":[64],"and":[65,83],"power":[66],"profile":[67],"rays":[69],"impinging":[70],"on":[71],"receiver":[73],"array.":[74],"validate":[76],"developed":[78],"method":[79],"via":[80],"numerical":[81],"simulations":[82],"demonstrate":[84],"an":[85],"improved":[86],"performance":[88],"relative":[89],"existing":[92],"methods.":[93]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}