{"id":"https://openalex.org/W7123362990","doi":"https://doi.org/10.1109/access.2026.3651725","title":"Device-Free Wi-Fi Localization via Deep Learning on Two-Way CSI","display_name":"Device-Free Wi-Fi Localization via Deep Learning on Two-Way CSI","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7123362990","doi":"https://doi.org/10.1109/access.2026.3651725"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3651725","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3651725","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3651725","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5115680262","display_name":"Omar Dhawahir","orcid":null},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Omar Dhawahir","raw_affiliation_strings":["Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX, USA","institution_ids":["https://openalex.org/I162577319"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091744317","display_name":"Murat Torlak","orcid":"https://orcid.org/0000-0001-7229-1765"},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Murat Torlak","raw_affiliation_strings":["Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX, USA","institution_ids":["https://openalex.org/I162577319"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5115680262"],"corresponding_institution_ids":["https://openalex.org/I162577319"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13330458,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"6747","last_page":"6756"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9387000203132629,"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":0.9387000203132629,"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/T11158","display_name":"Wireless Networks and Protocols","score":0.01119999960064888,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.006300000008195639,"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/channel-state-information","display_name":"Channel state information","score":0.5853000283241272},{"id":"https://openalex.org/keywords/jitter","display_name":"Jitter","score":0.5504999756813049},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5336999893188477},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49570000171661377},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4733999967575073},{"id":"https://openalex.org/keywords/multipath-propagation","display_name":"Multipath propagation","score":0.46959999203681946},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4415999948978424},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4401000142097473}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8291000127792358},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6324999928474426},{"id":"https://openalex.org/C148063708","wikidata":"https://www.wikidata.org/wiki/Q5072511","display_name":"Channel state information","level":3,"score":0.5853000283241272},{"id":"https://openalex.org/C134652429","wikidata":"https://www.wikidata.org/wiki/Q1052698","display_name":"Jitter","level":2,"score":0.5504999756813049},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5336999893188477},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49570000171661377},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4733999967575073},{"id":"https://openalex.org/C161218011","wikidata":"https://www.wikidata.org/wiki/Q11827794","display_name":"Multipath propagation","level":3,"score":0.46959999203681946},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4415999948978424},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4401000142097473},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.3756999969482422},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3747999966144562},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.34060001373291016},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3239000141620636},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.30550000071525574},{"id":"https://openalex.org/C44280652","wikidata":"https://www.wikidata.org/wiki/Q104837","display_name":"Phase (matter)","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C177860922","wikidata":"https://www.wikidata.org/wiki/Q788608","display_name":"Decorrelation","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2728999853134155},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C165443888","wikidata":"https://www.wikidata.org/wiki/Q1482183","display_name":"Transformation matrix","level":3,"score":0.2574999928474426},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.2563999891281128}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3651725","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3651725","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:4efa5214efb247f3a2a2a738493858eb","is_oa":true,"landing_page_url":"https://doaj.org/article/4efa5214efb247f3a2a2a738493858eb","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 6747-6756 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3651725","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3651725","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W2002475595","https://openalex.org/W2110774823","https://openalex.org/W2145732734","https://openalex.org/W2309512289","https://openalex.org/W2340862004","https://openalex.org/W2508522990","https://openalex.org/W2591820339","https://openalex.org/W2639224480","https://openalex.org/W2743415265","https://openalex.org/W2780385833","https://openalex.org/W2786484969","https://openalex.org/W2792651740","https://openalex.org/W2792767739","https://openalex.org/W2809578962","https://openalex.org/W2820000974","https://openalex.org/W2830017456","https://openalex.org/W2899430105","https://openalex.org/W2902516214","https://openalex.org/W2907057563","https://openalex.org/W2924475988","https://openalex.org/W2927133259","https://openalex.org/W2943650512","https://openalex.org/W2965062122","https://openalex.org/W3012049127","https://openalex.org/W3160315831","https://openalex.org/W3215616827","https://openalex.org/W4214813322","https://openalex.org/W4237031056","https://openalex.org/W4285299577","https://openalex.org/W4313270754","https://openalex.org/W4361857361","https://openalex.org/W4392941718","https://openalex.org/W4392942920","https://openalex.org/W4405844447"],"related_works":[],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,29,35,47,94],"novel":[3],"method":[4,55],"for":[5],"device-free":[6],"human":[7],"detection":[8],"and":[9,26,40,44,72,81,107,124,139,141],"localization":[10],"relative":[11],"to":[12,144],"reference":[13,90],"points,":[14],"leveraging":[15],"two-way":[16,36],"Wi-Fi":[17],"channel":[18],"state":[19],"information":[20],"(CSI)":[21],"obtained":[22],"from":[23],"DATA/ACK":[24],"exchanges":[25],"captured":[27],"by":[28],"single-antenna":[30],"passive":[31],"receiver.":[32],"By":[33],"constructing":[34],"CSI":[37],"cross-correlation":[38],"matrix":[39],"feeding":[41],"its":[42],"magnitude":[43],"phase":[45],"into":[46],"compact":[48],"one-dimensional":[49],"convolutional":[50],"neural":[51],"network":[52],"(CNN),":[53],"the":[54],"amplifies":[56],"subtle":[57],"human-induced":[58],"multipath":[59],"variations":[60],"while":[61],"remaining":[62],"fully":[63],"network-independent.":[64],"The":[65,129],"system":[66],"is":[67,142],"evaluated":[68],"in":[69],"diverse":[70],"environments":[71],"compared":[73],"with":[74],"conventional":[75],"classifiers,":[76],"including":[77],"k-nearest":[78],"neighbors":[79],"(KNN)":[80],"support":[82],"vector":[83],"machines":[84],"(SVM).":[85],"Outdoor":[86],"experiments":[87],"over":[88],"27":[89],"points":[91],"(RPs)":[92],"achieve":[93],"best":[95,119],"test":[96,120],"accuracy":[97],"of":[98,122],"96.22%":[99],"(mean":[100],"error":[101],"\u2248":[102],"1m),":[103],"outperforming":[104],"KNN":[105],"(37.83%)":[106],"SVM":[108],"(91.88%).":[109],"In":[110],"two":[111],"indoor":[112],"scenarios":[113],"(passive":[114],"receiver":[115],"inside":[116],"vs.":[117],"outside),":[118],"accuracies":[121],"98.36%":[123],"98.86%":[125],"are":[126],"obtained,":[127],"respectively.":[128],"approach":[130],"requires":[131],"no":[132],"AP/client":[133],"modifications,":[134],"generalizes":[135],"across":[136],"device":[137],"positions":[138],"days,":[140],"robust":[143],"ACK":[145],"timing":[146],"jitter":[147],"around":[148],"SIFS.":[149]},"counts_by_year":[],"updated_date":"2026-03-13T16:22:10.518609","created_date":"2026-01-14T00:00:00"}
