{"id":"https://openalex.org/W4226068131","doi":"https://doi.org/10.1109/wcnc51071.2022.9771668","title":"Millimeter Wave Localization with Imperfect Training Data using Shallow Neural Networks","display_name":"Millimeter Wave Localization with Imperfect Training Data using Shallow Neural Networks","publication_year":2022,"publication_date":"2022-04-10","ids":{"openalex":"https://openalex.org/W4226068131","doi":"https://doi.org/10.1109/wcnc51071.2022.9771668"},"language":"en","primary_location":{"id":"doi:10.1109/wcnc51071.2022.9771668","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc51071.2022.9771668","pdf_url":null,"source":{"id":"https://openalex.org/S4363607776","display_name":"2022 IEEE Wireless Communications and Networking Conference (WCNC)","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":"2022 IEEE Wireless Communications and Networking Conference (WCNC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/11572/345803","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":["University of Trento,DISI,Trento,Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Trento,DISI,Trento,Italy","institution_ids":["https://openalex.org/I193223587"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091154269","display_name":"Joan Palacios","orcid":"https://orcid.org/0000-0002-7324-7149"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joan Palacios","raw_affiliation_strings":["North Carolina State University,Raleigh,NC,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North Carolina State University,Raleigh,NC,USA","institution_ids":["https://openalex.org/I137902535"]}]},{"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":["University of Trento,DISI,Trento,Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Trento,DISI,Trento,Italy","institution_ids":["https://openalex.org/I193223587"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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":0.9994999766349792,"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":0.9994999766349792,"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":0.9977999925613403,"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":0.9659000039100647,"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/computer-science","display_name":"Computer science","score":0.780353844165802},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.703935980796814},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6603811383247375},{"id":"https://openalex.org/keywords/imperfect","display_name":"Imperfect","score":0.5624274015426636},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5545345544815063},{"id":"https://openalex.org/keywords/extremely-high-frequency","display_name":"Extremely high frequency","score":0.5152226090431213},{"id":"https://openalex.org/keywords/millimeter","display_name":"Millimeter","score":0.47471559047698975},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4704795181751251},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4332219064235687},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32940900325775146},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.328239381313324},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3238930106163025},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.2140444815158844}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.780353844165802},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.703935980796814},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6603811383247375},{"id":"https://openalex.org/C2780310539","wikidata":"https://www.wikidata.org/wiki/Q12547192","display_name":"Imperfect","level":2,"score":0.5624274015426636},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5545345544815063},{"id":"https://openalex.org/C45764600","wikidata":"https://www.wikidata.org/wiki/Q570342","display_name":"Extremely high frequency","level":2,"score":0.5152226090431213},{"id":"https://openalex.org/C109792285","wikidata":"https://www.wikidata.org/wiki/Q174789","display_name":"Millimeter","level":2,"score":0.47471559047698975},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4704795181751251},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4332219064235687},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32940900325775146},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.328239381313324},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3238930106163025},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.2140444815158844},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/wcnc51071.2022.9771668","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc51071.2022.9771668","pdf_url":null,"source":{"id":"https://openalex.org/S4363607776","display_name":"2022 IEEE Wireless Communications and Networking Conference (WCNC)","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":"2022 IEEE Wireless Communications and Networking Conference (WCNC)","raw_type":"proceedings-article"},{"id":"pmh:oai:iris.unitn.it:11572/345803","is_oa":true,"landing_page_url":"http://hdl.handle.net/11572/345803","pdf_url":null,"source":{"id":"https://openalex.org/S4377196320","display_name":"Iris (University of 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/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:iris.unitn.it:11572/345803","is_oa":true,"landing_page_url":"http://hdl.handle.net/11572/345803","pdf_url":null,"source":{"id":"https://openalex.org/S4377196320","display_name":"Iris (University of 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/conferenceObject"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2116334496","https://openalex.org/W2138020668","https://openalex.org/W2343842388","https://openalex.org/W2580872720","https://openalex.org/W2767057835","https://openalex.org/W2787191418","https://openalex.org/W2898264878","https://openalex.org/W2909204560","https://openalex.org/W2917561422","https://openalex.org/W2962920357","https://openalex.org/W3009202881","https://openalex.org/W3009570630","https://openalex.org/W3009761689","https://openalex.org/W3013280369","https://openalex.org/W3022862112","https://openalex.org/W6687483927"],"related_works":["https://openalex.org/W1383942","https://openalex.org/W13158784","https://openalex.org/W18481575","https://openalex.org/W11185966","https://openalex.org/W10917997","https://openalex.org/W1614943","https://openalex.org/W16400594","https://openalex.org/W14477910","https://openalex.org/W7946549","https://openalex.org/W18123511"],"abstract_inverted_index":{"Millimeter":[0],"wave":[1],"(mmWave)":[2],"localization":[3,24,135],"algorithms":[4],"exploit":[5],"the":[6,18,29,32,35,38,57,99,145],"quasi-optical":[7],"propagation":[8],"of":[9,31,37,59],"mmWave":[10,61,86,134],"signals,":[11],"which":[12],"yields":[13],"sparse":[14],"angular":[15],"spectra":[16],"at":[17],"receiver.":[19],"Geometric":[20],"approaches":[21],"to":[22,27,46,51,84,115,120],"angle-based":[23],"typically":[25],"require":[26],"know":[28],"map":[30],"environment":[33],"and":[34,110],"location":[36,55,130],"access":[39],"points.":[40],"Thus,":[41],"several":[42],"works":[43],"have":[44],"resorted":[45],"automated":[47],"learning":[48],"in":[49,98,107,138],"order":[50],"infer":[52],"a":[53,71,79],"device’s":[54],"from":[56,132],"properties":[58],"received":[60],"signals.":[62],"However,":[63],"collecting":[64],"training":[65,113,122],"data":[66,123],"for":[67,105],"such":[68],"models":[69],"is":[70,103],"significant":[72],"burden.":[73],"In":[74],"this":[75,139],"work,":[76],"we":[77],"propose":[78,119],"shallow":[80],"neural":[81,147],"network":[82],"model":[83,90],"localize":[85],"devices":[87],"indoors.":[88],"This":[89],"requires":[91],"significantly":[92],"fewer":[93,112],"weights":[94],"than":[95,155],"those":[96],"proposed":[97,146],"literature.":[100],"Therefore,":[101],"it":[102],"amenable":[104],"implementation":[106],"resource-constrained":[108],"hardware,":[109],"needs":[111],"samples":[114],"converge.":[116],"We":[117],"also":[118],"relieve":[121],"collection":[124],"efforts":[125],"by":[126],"retrieving":[127],"(inherently":[128],"imperfect)":[129],"estimates":[131],"geometry-based":[133],"algorithms.":[136,157],"Even":[137],"case,":[140],"our":[141],"results":[142],"show":[143],"that":[144],"networks":[148],"perform":[149],"as":[150,152],"good":[151],"or":[153],"better":[154],"state-of-the-art":[156]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2022-05-05T00:00:00"}