{"id":"https://openalex.org/W4392666375","doi":"https://doi.org/10.1109/lcomm.2024.3376150","title":"Indoor Millimeter Wave Localization Using Multiple Self-Supervised Tiny Neural Networks","display_name":"Indoor Millimeter Wave Localization Using Multiple Self-Supervised Tiny Neural Networks","publication_year":2024,"publication_date":"2024-03-11","ids":{"openalex":"https://openalex.org/W4392666375","doi":"https://doi.org/10.1109/lcomm.2024.3376150"},"language":"en","primary_location":{"id":"doi:10.1109/lcomm.2024.3376150","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lcomm.2024.3376150","pdf_url":null,"source":{"id":"https://openalex.org/S147316732","display_name":"IEEE Communications Letters","issn_l":"1089-7798","issn":["1089-7798","1558-2558","2373-7891"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316002","host_organization_name":"IEEE Communications Society","host_organization_lineage":["https://openalex.org/P4310316002","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Communications Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Communications Letters","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://iris.unitn.it/bitstream/11572/405830/1/Indoor_Millimeter_Wave_Localization_using_Multiple_Self-Supervised_Tiny_Neural_Networks.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065513913","display_name":"Anish Shastri","orcid":"https://orcid.org/0000-0002-9352-5417"},"institutions":[{"id":"https://openalex.org/I193223587","display_name":"University of Trento","ror":"https://ror.org/05trd4x28","country_code":"IT","type":"education","lineage":["https://openalex.org/I193223587"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Anish Shastri","raw_affiliation_strings":["Department of Information Engineering and Computer Science, University of Trento, Trento, Italy"],"raw_orcid":"https://orcid.org/0000-0002-9352-5417","affiliations":[{"raw_affiliation_string":"Department of Information Engineering and Computer Science, University of Trento, Trento, Italy","institution_ids":["https://openalex.org/I193223587"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073770360","display_name":"Andr\u00e9s Garc\u00eda\u2010Saavedra","orcid":"https://orcid.org/0000-0003-2005-2222"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andres Garcia-Saavedra","raw_affiliation_strings":["NEC Laboratories Europe, Heidelberg, Germany"],"raw_orcid":"https://orcid.org/0000-0003-2005-2222","affiliations":[{"raw_affiliation_string":"NEC Laboratories Europe, Heidelberg, Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026873556","display_name":"Paolo Casari","orcid":"https://orcid.org/0000-0002-6401-1660"},"institutions":[{"id":"https://openalex.org/I193223587","display_name":"University of Trento","ror":"https://ror.org/05trd4x28","country_code":"IT","type":"education","lineage":["https://openalex.org/I193223587"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Paolo Casari","raw_affiliation_strings":["Department of Information Engineering and Computer Science, University of Trento, Trento, Italy"],"raw_orcid":"https://orcid.org/0000-0002-6401-1660","affiliations":[{"raw_affiliation_string":"Department of Information Engineering and Computer Science, University of Trento, Trento, Italy","institution_ids":["https://openalex.org/I193223587"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0385,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.7504791,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"28","issue":"5","first_page":"1034","last_page":"1038"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.9993000030517578,"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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.9993000030517578,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9987999796867371,"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/T13121","display_name":"Radio Wave Propagation Studies","score":0.9968000054359436,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/extremely-high-frequency","display_name":"Extremely high frequency","score":0.6460330486297607},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5869985222816467},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5782265663146973},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3610017001628876},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.31469929218292236}],"concepts":[{"id":"https://openalex.org/C45764600","wikidata":"https://www.wikidata.org/wiki/Q570342","display_name":"Extremely high frequency","level":2,"score":0.6460330486297607},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5869985222816467},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5782265663146973},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3610017001628876},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.31469929218292236}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/lcomm.2024.3376150","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lcomm.2024.3376150","pdf_url":null,"source":{"id":"https://openalex.org/S147316732","display_name":"IEEE Communications Letters","issn_l":"1089-7798","issn":["1089-7798","1558-2558","2373-7891"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316002","host_organization_name":"IEEE Communications Society","host_organization_lineage":["https://openalex.org/P4310316002","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Communications Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Communications Letters","raw_type":"journal-article"},{"id":"pmh:oai:iris.unitn.it:11572/405830","is_oa":true,"landing_page_url":"https://hdl.handle.net/11572/405830","pdf_url":"https://iris.unitn.it/bitstream/11572/405830/1/Indoor_Millimeter_Wave_Localization_using_Multiple_Self-Supervised_Tiny_Neural_Networks.pdf","source":{"id":"https://openalex.org/S4306401913","display_name":"Institutional Research Information System (Universit\u00e0 degli Studi di Trento)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I193223587","host_organization_name":"University of Trento","host_organization_lineage":["https://openalex.org/I193223587"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:iris.unitn.it:11572/405830","is_oa":true,"landing_page_url":"https://hdl.handle.net/11572/405830","pdf_url":"https://iris.unitn.it/bitstream/11572/405830/1/Indoor_Millimeter_Wave_Localization_using_Multiple_Self-Supervised_Tiny_Neural_Networks.pdf","source":{"id":"https://openalex.org/S4306401913","display_name":"Institutional Research Information System (Universit\u00e0 degli Studi di Trento)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I193223587","host_organization_name":"University of Trento","host_organization_lineage":["https://openalex.org/I193223587"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8704386357","display_name":"Millimeter-wave Networking and Sensing for Beyond 5G","funder_award_id":"861222","funder_id":"https://openalex.org/F4320338337","funder_display_name":"H2020 Marie Sk\u0142odowska-Curie Actions"}],"funders":[{"id":"https://openalex.org/F4320338337","display_name":"H2020 Marie Sk\u0142odowska-Curie Actions","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4392666375.pdf","grobid_xml":"https://content.openalex.org/works/W4392666375.grobid-xml"},"referenced_works_count":14,"referenced_works":["https://openalex.org/W2105934661","https://openalex.org/W2580872720","https://openalex.org/W2771579582","https://openalex.org/W2915274585","https://openalex.org/W2917561422","https://openalex.org/W3004719051","https://openalex.org/W3009570630","https://openalex.org/W3022862112","https://openalex.org/W3031405364","https://openalex.org/W3191155192","https://openalex.org/W4280564052","https://openalex.org/W4283022747","https://openalex.org/W4285203226","https://openalex.org/W4372266154"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"We":[0,102],"consider":[1],"the":[2,52,56,67,73,84,95,99,104,119],"localization":[3,74,116],"of":[4,21,98,121],"a":[5,10,25,39,88,122],"mobile":[6],"mmw":[7],"client":[8],"in":[9,38],"large":[11],"indoor":[12],"environment":[13],"using":[14],"multilayer":[15],"perceptron":[16],"neural":[17],"networks":[18],"(NNs).":[19],"Instead":[20],"training":[22,100],"and":[23,49,91,118],"deploying":[24],"single":[26,123],"deep":[27],"model,":[28],"we":[29,76],"proceed":[30],"by":[31,87],"choosing":[32],"among":[33,55],"multiple":[34],"tiny":[35],"NNs":[36],"trained":[37],"self-supervised":[40],"manner.":[41],"The":[42],"main":[43],"challenge":[44],"is":[45],"then":[46],"to":[47,51,65,71],"determine":[48],"switch":[50],"best":[53],"NN":[54,62],"available":[57],"ones,":[58],"as":[59],"an":[60],"incorrect":[61],"will":[63],"fail":[64],"localize":[66],"client.":[68],"In":[69],"order":[70],"preserve":[72],"accuracy,":[75],"propose":[77],"two":[78],"switching":[79],"schemes:":[80],"one":[81,92],"based":[82,93],"on":[83,94],"innovation":[85],"measured":[86],"Kalman":[89],"filter,":[90],"statistical":[96],"distribution":[97],"data.":[101],"analyze":[103],"proposed":[105],"schemes":[106,117],"via":[107],"simulations,":[108],"showing":[109],"that":[110],"our":[111],"approach":[112],"outperforms":[113],"both":[114],"geometric":[115],"use":[120],"NN.":[124]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}