{"id":"https://openalex.org/W7143333728","doi":"https://doi.org/10.1109/tcomm.2026.3679225","title":"An Adaptive Spatio-Temporal Deep Learning Algorithm for Indoor Positioning With MIMO-OFDM Systems","display_name":"An Adaptive Spatio-Temporal Deep Learning Algorithm for Indoor Positioning With MIMO-OFDM Systems","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7143333728","doi":"https://doi.org/10.1109/tcomm.2026.3679225"},"language":null,"primary_location":{"id":"doi:10.1109/tcomm.2026.3679225","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcomm.2026.3679225","pdf_url":null,"source":{"id":"https://openalex.org/S196647941","display_name":"IEEE Transactions on Communications","issn_l":"0090-6778","issn":["0090-6778","1558-0857"],"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 Transactions on Communications","raw_type":"journal-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/A5130135577","display_name":"Zihao Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zihao Ding","raw_affiliation_strings":["National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024356299","display_name":"Han Ji","orcid":"https://orcid.org/0000-0003-2581-6316"},"institutions":[{"id":"https://openalex.org/I4210155350","display_name":"Purple Mountain Laboratories","ror":"https://ror.org/04zcbk583","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210155350"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Han Ji","raw_affiliation_strings":["Pervasive Communication Research Center, Purple Mountain Laboratories, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Pervasive Communication Research Center, Purple Mountain Laboratories, Nanjing, China","institution_ids":["https://openalex.org/I4210155350"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003738879","display_name":"Ying Yang","orcid":"https://orcid.org/0000-0002-3573-4122"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiye Yang","raw_affiliation_strings":["National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiping Wu","orcid":"https://orcid.org/0000-0001-5794-2910"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiping Wu","raw_affiliation_strings":["National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":null,"display_name":"Cheng-Xiang Wang","orcid":"https://orcid.org/0000-0002-9729-9592"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng-Xiang Wang","raw_affiliation_strings":["National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5130135577"],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.8025737,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"74","issue":null,"first_page":"6874","last_page":"6888"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9089999794960022,"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":0.9089999794960022,"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/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.0066999997943639755,"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"}},{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.006099999882280827,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/deep-learning","display_name":"Deep learning","score":0.6809999942779541},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.3564999997615814},{"id":"https://openalex.org/keywords/algorithm-design","display_name":"Algorithm design","score":0.35409998893737793},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.33970001339912415},{"id":"https://openalex.org/keywords/nasa-deep-space-network","display_name":"NASA Deep Space Network","score":0.3190000057220459},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.29249998927116394}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6809999942779541},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6769999861717224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5702000260353088},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.510200023651123},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.37389999628067017},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.3564999997615814},{"id":"https://openalex.org/C106516650","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm design","level":2,"score":0.35409998893737793},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33970001339912415},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33079999685287476},{"id":"https://openalex.org/C187107819","wikidata":"https://www.wikidata.org/wiki/Q835696","display_name":"NASA Deep Space Network","level":3,"score":0.3190000057220459},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.29249998927116394},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.2881999909877777},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C102248274","wikidata":"https://www.wikidata.org/wiki/Q168388","display_name":"Adaptive filter","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.2538999915122986}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcomm.2026.3679225","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcomm.2026.3679225","pdf_url":null,"source":{"id":"https://openalex.org/S196647941","display_name":"IEEE Transactions on Communications","issn_l":"0090-6778","issn":["0090-6778","1558-0857"],"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 Transactions on Communications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4089609767","display_name":null,"funder_award_id":"2025M783538","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G6242755693","display_name":null,"funder_award_id":"2025ZD1304900","funder_id":"https://openalex.org/F4320329860","funder_display_name":"National Science and Technology Major Project"}],"funders":[{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"},{"id":"https://openalex.org/F4320329860","display_name":"National Science and Technology Major Project","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0,102],"work":[1],"studies":[2],"learning-aided":[3],"indoor":[4],"fingerprint":[5],"positioning":[6,82,168,183],"(FP)":[7],"for":[8],"multiple-input":[9],"multiple-output":[10],"(MIMO)":[11],"orthogonal":[12],"frequency-division":[13],"multiplexing":[14],"(OFDM)":[15],"systems,":[16],"by":[17,85],"exploiting":[18],"their":[19,48],"channel":[20],"response":[21],"matrices":[22],"(CRMs).":[23],"The":[24],"existing":[25,186],"learning":[26],"methods":[27],"of":[28,53,105,119,172],"CRM-based":[29],"FP":[30,94,187],"mostly":[31],"suffer":[32],"from":[33],"two":[34,106],"main":[35],"limitations:":[36],"i)":[37,109],"they":[38,64],"require":[39],"retraining":[40],"when":[41],"CRM":[42],"dimensions":[43,121],"change,":[44],"which":[45],"severely":[46],"limits":[47],"practicality":[49],"since":[50],"the":[51,66,72,133,163],"number":[52],"antennas":[54],"and":[55,140,143,155,174,199],"subcarriers":[56],"may":[57],"vary":[58],"dynamically":[59],"during":[60],"practical":[61],"implementation;":[62],"ii)":[63,144],"overlook":[65],"temporal":[67,156],"correlations":[68],"in":[69,159,182,189],"CRMs":[70,117,126],"along":[71,162],"user":[73],"trajectory,":[74,165],"failing":[75],"to":[76,80,136,151,191],"exploit":[77],"this":[78],"information":[79],"improve":[81],"accuracy.":[83,169],"Motivated":[84],"these":[86],"limitations,":[87],"we":[88],"propose":[89],"a":[90,129,179],"novel":[91],"deep":[92],"learning-assisted":[93],"method,":[95],"named":[96],"adaptive":[97,111],"spatio-temporal":[98],"neural":[99,134],"network":[100,135],"(A-STNN).":[101],"method":[103],"consists":[104],"key":[107],"components:":[108],"an":[110,145],"mechanism":[112],"that":[113,147],"transforms":[114],"spatial-frequency":[115],"domain":[116,125],"(SFCRMs)":[118],"arbitrary":[120],"into":[122],"truncated":[123],"angle-delay":[124],"(T-ADCRMs)":[127],"with":[128],"unified":[130],"dimension,":[131],"enabling":[132],"handle":[137],"varying":[138],"antenna":[139,198],"subcarrier":[141,200],"configurations;":[142],"STNN":[146],"exploits":[148],"attention":[149],"mechanisms":[150],"jointly":[152],"extract":[153],"spatial":[154],"features":[157],"embedded":[158],"T-ADCRM":[160],"sequences":[161],"user\u2019s":[164],"thus":[166],"improving":[167],"Upon":[170],"examination":[171],"simulation":[173],"measurement":[175],"datasets,":[176],"A-STNN":[177],"achieves":[178],"prominent":[180],"improvement":[181],"accuracy":[184],"over":[185],"methods,":[188],"addition":[190],"its":[192],"unique":[193],"generalization":[194],"capability":[195],"across":[196],"different":[197],"settings.":[201]},"counts_by_year":[],"updated_date":"2026-04-11T06:13:24.991567","created_date":"2026-03-31T00:00:00"}
