{"id":"https://openalex.org/W3005947811","doi":"https://doi.org/10.1109/uemcon47517.2019.8993100","title":"UWB Channel Classification Using Convolutional Neural Networks","display_name":"UWB Channel Classification Using Convolutional Neural Networks","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W3005947811","doi":"https://doi.org/10.1109/uemcon47517.2019.8993100","mag":"3005947811"},"language":"en","primary_location":{"id":"doi:10.1109/uemcon47517.2019.8993100","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uemcon47517.2019.8993100","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","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/A5052530172","display_name":"Parnian A. ShirinAbadi","orcid":null},"institutions":[{"id":"https://openalex.org/I167694869","display_name":"Bu-Ali Sina University","ror":"https://ror.org/04ka8rx28","country_code":"IR","type":"education","lineage":["https://openalex.org/I167694869"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Parnian A. ShirinAbadi","raw_affiliation_strings":["Department of Computer Scinece, BuAli Sina University, Hamedan, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Scinece, BuAli Sina University, Hamedan, Iran","institution_ids":["https://openalex.org/I167694869"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089238798","display_name":"Arash Abbasi","orcid":"https://orcid.org/0000-0002-4392-011X"},"institutions":[{"id":"https://openalex.org/I125467818","display_name":"Dakota State University","ror":"https://ror.org/016yv6y68","country_code":"US","type":"education","lineage":["https://openalex.org/I125467818"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arash Abbasi","raw_affiliation_strings":["Beacom College of Computer and Cyber Sciences, Dakota State University, South Dakota, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beacom College of Computer and Cyber Sciences, Dakota State University, South Dakota, USA","institution_ids":["https://openalex.org/I125467818"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.332,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.81869375,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1064","last_page":"1068"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"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/T12024","display_name":"Ultra-Wideband Communications Technology","score":0.9997000098228455,"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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.9947999715805054,"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/non-line-of-sight-propagation","display_name":"Non-line-of-sight propagation","score":0.9564406871795654},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.830079972743988},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8053305149078369},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6154348254203796},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.6049079895019531},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5075691938400269},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.494111567735672},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45461827516555786},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.4490507245063782},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41554561257362366},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.15806236863136292},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.14909610152244568},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.13042917847633362}],"concepts":[{"id":"https://openalex.org/C154910267","wikidata":"https://www.wikidata.org/wiki/Q1740982","display_name":"Non-line-of-sight propagation","level":3,"score":0.9564406871795654},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.830079972743988},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8053305149078369},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6154348254203796},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.6049079895019531},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5075691938400269},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.494111567735672},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45461827516555786},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.4490507245063782},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41554561257362366},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.15806236863136292},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.14909610152244568},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.13042917847633362},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/uemcon47517.2019.8993100","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uemcon47517.2019.8993100","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7300000190734863,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2069490399","https://openalex.org/W2163605009","https://openalex.org/W2165668427","https://openalex.org/W2230051723","https://openalex.org/W2402514617","https://openalex.org/W6667866181","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W4312417841","https://openalex.org/W2738221750","https://openalex.org/W4311257506","https://openalex.org/W2732542196","https://openalex.org/W564581980","https://openalex.org/W2337926734","https://openalex.org/W4366224123","https://openalex.org/W2963958939","https://openalex.org/W4320802194","https://openalex.org/W3173182854"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"a":[3],"novel":[4],"convolutional":[5],"neural":[6],"network":[7],"(CNN)":[8],"algorithm":[9],"for":[10,37,42,59,81],"ultra-wideband":[11],"(UWB)":[12],"line-of-sight":[13],"(LOS)":[14],"and":[15,33,88],"non-line-of-sight":[16],"(NLOS)":[17],"channel":[18],"classification":[19,38,43,60],"is":[20,74],"proposed.":[21],"Unlike":[22],"the":[23,45,54,65,71,77],"existing":[24],"methods,":[25],"which":[26,53],"are":[27],"based":[28],"on":[29],"machine":[30],"learning":[31,50],"algorithms":[32],"require":[34],"suitable":[35],"information/parameters":[36],"to":[39],"be":[40],"extracted":[41],"procedure,":[44],"proposed":[46,72],"method":[47,73],"uses":[48],"deep":[49],"approaches":[51],"in":[52,76,84],"model":[55],"learns":[56],"discriminating":[57],"information":[58],"automatically":[61],"by":[62],"itself":[63],"during":[64],"\u201ctraining\u201d":[66],"phase.":[67],"The":[68],"performance":[69],"of":[70],"investigated":[75],"IEEE":[78],"802.15.4a":[79],"standard":[80],"UWB":[82],"channels":[83],"indoor":[85],"office":[86],"LOS":[87],"NLOS":[89],"environments.":[90]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}