{"id":"https://openalex.org/W4415624625","doi":"https://doi.org/10.1109/ipin66788.2025.11213198","title":"Efficient LOS/NLOS Classification in 5G NR Using Field Image Transformation and Modified Mean Teacher Framework","display_name":"Efficient LOS/NLOS Classification in 5G NR Using Field Image Transformation and Modified Mean Teacher Framework","publication_year":2025,"publication_date":"2025-09-15","ids":{"openalex":"https://openalex.org/W4415624625","doi":"https://doi.org/10.1109/ipin66788.2025.11213198"},"language":null,"primary_location":{"id":"doi:10.1109/ipin66788.2025.11213198","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipin66788.2025.11213198","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Indoor Positioning and Indoor Navigation (IPIN)","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/A5101773785","display_name":"Ziheng Chen","orcid":"https://orcid.org/0000-0002-2585-637X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziheng Chen","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Electronic Engineering,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Electronic Engineering,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010176890","display_name":"Renhao Liu","orcid":"https://orcid.org/0000-0002-9081-0693"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Renhao Liu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Electronic Engineering,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Electronic Engineering,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102222671","display_name":"Zhongliang Deng","orcid":"https://orcid.org/0000-0002-1984-8613"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongliang Deng","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Electronic Engineering,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Electronic Engineering,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077221055","display_name":"Enwen Hu","orcid":"https://orcid.org/0000-0001-5019-3961"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Enwen Hu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Electronic Engineering,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Electronic Engineering,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037386169","display_name":"Yongheng Deng","orcid":"https://orcid.org/0000-0003-3010-3812"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongheng Deng","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Electronic Engineering,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Electronic Engineering,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"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":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9514999985694885,"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":0.9514999985694885,"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/T10655","display_name":"GNSS positioning and interference","score":0.009399999864399433,"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"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.004900000058114529,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/smoothing","display_name":"Smoothing","score":0.5177000164985657},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49970000982284546},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46970000863075256},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.42739999294281006},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.41029998660087585},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.37130001187324524},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.37119999527931213},{"id":"https://openalex.org/keywords/exponential-smoothing","display_name":"Exponential smoothing","score":0.3440000116825104}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6450999975204468},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6403999924659729},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.5177000164985657},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49970000982284546},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46970000863075256},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.42739999294281006},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.41029998660087585},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.37130001187324524},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.37119999527931213},{"id":"https://openalex.org/C133710760","wikidata":"https://www.wikidata.org/wiki/Q775837","display_name":"Exponential smoothing","level":2,"score":0.3440000116825104},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.33649998903274536},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.33500000834465027},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3181000053882599},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3061000108718872},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.301800012588501},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.29750001430511475},{"id":"https://openalex.org/C72279823","wikidata":"https://www.wikidata.org/wiki/Q1139726","display_name":"Impulse response","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C70836080","wikidata":"https://www.wikidata.org/wiki/Q837940","display_name":"Impulse (physics)","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25380000472068787},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ipin66788.2025.11213198","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipin66788.2025.11213198","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Indoor Positioning and Indoor Navigation (IPIN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2129088323","https://openalex.org/W2793043725","https://openalex.org/W2902750893","https://openalex.org/W3001909141","https://openalex.org/W3035328184","https://openalex.org/W3035433001","https://openalex.org/W3041421099","https://openalex.org/W3096445762","https://openalex.org/W3118145897","https://openalex.org/W3129694821","https://openalex.org/W3192862439","https://openalex.org/W3205693253","https://openalex.org/W4312382380","https://openalex.org/W4382007487","https://openalex.org/W4387400865","https://openalex.org/W4387682179"],"related_works":[],"abstract_inverted_index":{"5G":[0,180],"NR":[1,181],"wireless":[2],"positioning":[3,182],"demonstrates":[4],"exceptional":[5],"potential":[6],"in":[7,122,178,184],"indoor":[8],"and":[9,20,53,100,144],"GNSS-denied":[10],"environments":[11],"due":[12],"to":[13,59,113,126],"its":[14],"low":[15],"latency,":[16],"high":[17],"signal":[18,34],"quality,":[19],"precise":[21],"measurement":[22],"capabilities.":[23],"However,":[24],"non-line-of-sight":[25],"(NLOS)":[26],"propagation":[27],"significantly":[28],"degrades":[29],"performance,":[30],"necessitating":[31],"robust":[32],"LOS/NLOS":[33],"identification.":[35],"This":[36],"paper":[37],"presents":[38],"an":[39],"innovative":[40],"framework":[41,134],"combining":[42],"Gramian":[43],"Angular":[44],"Fields":[45],"(GAFs),":[46],"Recurrence":[47],"Plots":[48],"(RPs),":[49],"Kolmogorov-Arnold":[50],"Networks":[51],"(KAN),":[52],"a":[54,91,102,150],"Mean":[55,132],"Teacher":[56,133],"semi-supervised":[57,163],"approach":[58],"address":[60],"these":[61],"challenges.Our":[62],"method":[63],"converts":[64],"raw":[65],"channel":[66],"impulse":[67],"response":[68],"(CIR)":[69],"sequences":[70],"into":[71],"two-dimensional":[72],"Field":[73,98],"Images":[74],"through":[75,110,138],"GAF-GADF":[76],"encoding,":[77],"enabling":[78],"rich":[79],"spatial-temporal":[80],"feature":[81,85],"extraction.":[82],"A":[83,117],"hybrid":[84],"learning":[86,164],"strategy":[87],"is":[88,120],"employed:":[89],"(1)":[90],"lightweight":[92],"CNN":[93],"extracts":[94],"visual":[95],"features":[96,109],"from":[97],"Images,":[99],"(2)":[101],"KAN-based":[103],"module":[104,125],"processes":[105],"manually":[106],"extracted":[107],"CIR":[108],"spline-activated":[111],"layers":[112],"capture":[114],"nonlinear":[115],"relationships.":[116],"consistency":[118,145],"factor":[119],"introduced":[121],"the":[123,131,154,175],"fusion":[124],"enhance":[127],"model":[128],"updates,":[129],"while":[130,162],"leverages":[135],"unlabeled":[136],"data":[137],"exponential":[139],"moving":[140],"average":[141],"weight":[142],"smoothing":[143],"loss":[146],"regularization.Extensive":[147],"experiments":[148],"on":[149],"real-world":[151],"dataset":[152],"validate":[153],"proposed":[155],"approach:":[156],"supervised":[157],"classification":[158],"achieves":[159],"99.1%":[160],"accuracy,":[161,167],"attains":[165],"89.8%":[166],"both":[168],"surpassing":[169],"state-of-the-art":[170],"methods.":[171],"These":[172],"results":[173],"demonstrate":[174],"framework\u2019s":[176],"effectiveness":[177],"enhancing":[179],"reliability":[183],"obstructive":[185],"environments.":[186]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-28T00:00:00"}