{"id":"https://openalex.org/W3171206404","doi":"https://doi.org/10.1109/vtc2021-spring51267.2021.9448839","title":"Convolutional Neural Networks based Denoising for Indoor Localization","display_name":"Convolutional Neural Networks based Denoising for Indoor Localization","publication_year":2021,"publication_date":"2021-04-01","ids":{"openalex":"https://openalex.org/W3171206404","doi":"https://doi.org/10.1109/vtc2021-spring51267.2021.9448839","mag":"3171206404"},"language":"en","primary_location":{"id":"doi:10.1109/vtc2021-spring51267.2021.9448839","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2021-spring51267.2021.9448839","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","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/A5017265393","display_name":"Wafa Njima","orcid":"https://orcid.org/0000-0002-8700-3161"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210142324","display_name":"CY Cergy Paris Universit\u00e9","ror":"https://ror.org/043htjv09","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210142324"]},{"id":"https://openalex.org/I86175216","display_name":"\u00c9cole Nationale Sup\u00e9rieure de l'\u00c9lectronique et de ses Applications","ror":"https://ror.org/03qeacd72","country_code":"FR","type":"education","lineage":["https://openalex.org/I86175216"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Wafa Njima","raw_affiliation_strings":["ETIS, UMR8051, CY Cergy Paris Universit\u00e9, ENSEA, CNRS, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ETIS, UMR8051, CY Cergy Paris Universit\u00e9, ENSEA, CNRS, France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I4210142324","https://openalex.org/I86175216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026039370","display_name":"Marwa Chafii","orcid":"https://orcid.org/0000-0003-0314-6585"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210142324","display_name":"CY Cergy Paris Universit\u00e9","ror":"https://ror.org/043htjv09","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210142324"]},{"id":"https://openalex.org/I86175216","display_name":"\u00c9cole Nationale Sup\u00e9rieure de l'\u00c9lectronique et de ses Applications","ror":"https://ror.org/03qeacd72","country_code":"FR","type":"education","lineage":["https://openalex.org/I86175216"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Marwa Chafii","raw_affiliation_strings":["ETIS, UMR8051, CY Cergy Paris Universit\u00e9, ENSEA, CNRS, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ETIS, UMR8051, CY Cergy Paris Universit\u00e9, ENSEA, CNRS, France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I4210142324","https://openalex.org/I86175216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031956158","display_name":"Ahmad Nimr","orcid":"https://orcid.org/0000-0003-3045-6271"},"institutions":[{"id":"https://openalex.org/I245417339","display_name":"Vodafone (Germany)","ror":"https://ror.org/044qwkx83","country_code":"DE","type":"company","lineage":["https://openalex.org/I245417339","https://openalex.org/I74316835"]},{"id":"https://openalex.org/I78650965","display_name":"Technische Universit\u00e4t Dresden","ror":"https://ror.org/042aqky30","country_code":"DE","type":"education","lineage":["https://openalex.org/I78650965"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ahmad Nimr","raw_affiliation_strings":["Vodafone Chair Mobile Communication Systems, Technische Universitat Dresden, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vodafone Chair Mobile Communication Systems, Technische Universitat Dresden, Germany","institution_ids":["https://openalex.org/I245417339","https://openalex.org/I78650965"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022117288","display_name":"Gerhard Fettweis","orcid":"https://orcid.org/0000-0003-4622-1311"},"institutions":[{"id":"https://openalex.org/I245417339","display_name":"Vodafone (Germany)","ror":"https://ror.org/044qwkx83","country_code":"DE","type":"company","lineage":["https://openalex.org/I245417339","https://openalex.org/I74316835"]},{"id":"https://openalex.org/I78650965","display_name":"Technische Universit\u00e4t Dresden","ror":"https://ror.org/042aqky30","country_code":"DE","type":"education","lineage":["https://openalex.org/I78650965"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Gerhard Fettweis","raw_affiliation_strings":["Vodafone Chair Mobile Communication Systems, Technische Universitat Dresden, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vodafone Chair Mobile Communication Systems, Technische Universitat Dresden, Germany","institution_ids":["https://openalex.org/I245417339","https://openalex.org/I78650965"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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/T11192","display_name":"Underwater Vehicles and Communication Systems","score":0.9970999956130981,"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/T10860","display_name":"Speech and Audio Processing","score":0.9930999875068665,"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/trilateration","display_name":"Trilateration","score":0.9258498549461365},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.685207724571228},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6381869912147522},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.5787429809570312},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.5593755841255188},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5588760375976562},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5341941714286804},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.509320855140686},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49959540367126465},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.481950968503952},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44449037313461304},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44329845905303955},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42293262481689453},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07582834362983704}],"concepts":[{"id":"https://openalex.org/C20832178","wikidata":"https://www.wikidata.org/wiki/Q1146298","display_name":"Trilateration","level":3,"score":0.9258498549461365},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.685207724571228},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6381869912147522},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.5787429809570312},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.5593755841255188},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5588760375976562},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5341941714286804},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.509320855140686},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49959540367126465},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.481950968503952},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44449037313461304},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44329845905303955},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42293262481689453},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07582834362983704},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtc2021-spring51267.2021.9448839","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2021-spring51267.2021.9448839","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.5899999737739563}],"awards":[{"id":"https://openalex.org/G6748708539","display_name":"PSI","funder_award_id":"ANR-16-IDEX-0008","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"}],"funders":[{"id":"https://openalex.org/F4320320883","display_name":"Agence Nationale de la Recherche","ror":"https://ror.org/00rbzpz17"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1747229408","https://openalex.org/W2038070995","https://openalex.org/W2100971368","https://openalex.org/W2124216931","https://openalex.org/W2133710940","https://openalex.org/W2140603325","https://openalex.org/W2143228105","https://openalex.org/W2535073990","https://openalex.org/W2769978888","https://openalex.org/W2903425833","https://openalex.org/W2944658523","https://openalex.org/W2950692821","https://openalex.org/W2964121744","https://openalex.org/W3088918359","https://openalex.org/W3099019051","https://openalex.org/W6631190155"],"related_works":["https://openalex.org/W1707434239","https://openalex.org/W3130929031","https://openalex.org/W2248192341","https://openalex.org/W2790716548","https://openalex.org/W2085095272","https://openalex.org/W2337616151","https://openalex.org/W2761350175","https://openalex.org/W2091347175","https://openalex.org/W2218990805","https://openalex.org/W3213338949"],"abstract_inverted_index":{"Indoor":[0],"localization":[1,133],"can":[2],"be":[3],"based":[4,95,106],"on":[5,69,86,96,107],"a":[6,87,91],"matrix":[7,19,74,103],"of":[8,129],"pairwise":[9],"distances":[10],"between":[11],"nodes":[12],"to":[13,29,36,51,58,76],"localize":[14],"and":[15,24,98,132],"reference":[16],"nodes.":[17],"This":[18,81],"is":[20,27,66,84],"usually":[21],"not":[22],"complete,":[23],"its":[25],"completion":[26,104],"subject":[28],"distance":[30,73],"estimation":[31,111],"errors":[32],"as":[33,35],"well":[34],"the":[37,60,70,101,108,124],"noise":[38],"resulting":[39],"from":[40],"received":[41],"signal":[42],"strength":[43],"indicator":[44],"measurements.":[45],"In":[46],"this":[47],"paper,":[48],"we":[49],"propose":[50],"use":[52],"convolutional":[53],"neural":[54],"networks":[55],"in":[56,127],"order":[57],"denoise":[59],"completed":[61],"matrix.":[62],"A":[63],"trilateration":[64],"process":[65],"then":[67],"applied":[68],"recovered":[71],"euclidean":[72],"(EDM)":[75],"locate":[77],"an":[78],"unknown":[79],"node.":[80],"proposed":[82],"approach":[83],"tested":[85],"simulated":[88],"environment,":[89],"using":[90],"real":[92],"propagation":[93],"model":[94],"measurements,":[97],"compared":[99],"with":[100,114],"classical":[102,125],"approach,":[105],"adaptive":[109],"moment":[110],"method,":[112],"combined":[113],"trilateration.":[115],"The":[116],"simulation":[117],"results":[118],"show":[119],"that":[120],"our":[121],"system":[122],"outperforms":[123],"schemes":[126],"terms":[128],"EDM":[130],"recovery":[131],"accuracy.":[134]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}