{"id":"https://openalex.org/W4415524520","doi":"https://doi.org/10.1109/mlsp62443.2025.11204227","title":"U-Net Based Indoor Radio Map Prediction Under Sparse Sampling","display_name":"U-Net Based Indoor Radio Map Prediction Under Sparse Sampling","publication_year":2025,"publication_date":"2025-08-31","ids":{"openalex":"https://openalex.org/W4415524520","doi":"https://doi.org/10.1109/mlsp62443.2025.11204227"},"language":null,"primary_location":{"id":"doi:10.1109/mlsp62443.2025.11204227","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp62443.2025.11204227","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 35th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-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/A5111097612","display_name":"Tianxiang Xing","orcid":null},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianxiang Xing","raw_affiliation_strings":["University of California,Electrical and Computer Engineering Department,Los Angeles,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California,Electrical and Computer Engineering Department,Los Angeles,USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Leyi Zou","orcid":null},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Leyi Zou","raw_affiliation_strings":["University of California,Electrical and Computer Engineering Department,Los Angeles,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California,Electrical and Computer Engineering Department,Los Angeles,USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033888911","display_name":"Tejas Bharadwaj","orcid":null},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tejas Bharadwaj","raw_affiliation_strings":["University of California,Electrical and Computer Engineering Department,Los Angeles,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California,Electrical and Computer Engineering Department,Los Angeles,USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089242792","display_name":"Rushabha Balaji","orcid":"https://orcid.org/0009-0002-7115-5432"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rushabha Balaji","raw_affiliation_strings":["University of California,Electrical and Computer Engineering Department,Los Angeles,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California,Electrical and Computer Engineering Department,Los Angeles,USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008128583","display_name":"Danijela \u010cabri\u0107","orcid":"https://orcid.org/0000-0002-5967-2683"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Danijela \u010cabri\u0107","raw_affiliation_strings":["University of California,Electrical and Computer Engineering Department,Los Angeles,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California,Electrical and Computer Engineering Department,Los Angeles,USA","institution_ids":["https://openalex.org/I161318765"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.128,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.89339666,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"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/T10860","display_name":"Speech and Audio Processing","score":0.9968000054359436,"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"}},"topics":[{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9968000054359436,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9947999715805054,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9467999935150146,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/context","display_name":"Context (archaeology)","score":0.6190999746322632},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5354999899864197},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5266000032424927},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46129998564720154},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3977000117301941},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.37220001220703125},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.349700003862381}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6582000255584717},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6190999746322632},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5672000050544739},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5354999899864197},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5266000032424927},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46129998564720154},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3977000117301941},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.37220001220703125},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.349700003862381},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33899998664855957},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3375000059604645},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.26750001311302185},{"id":"https://openalex.org/C11577676","wikidata":"https://www.wikidata.org/wiki/Q134237","display_name":"Square root","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.2574999928474426},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25679999589920044}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp62443.2025.11204227","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp62443.2025.11204227","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 35th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1997834106","https://openalex.org/W2027624916","https://openalex.org/W2111308925","https://openalex.org/W2143738441","https://openalex.org/W3015635818","https://openalex.org/W3131967102","https://openalex.org/W4392931203","https://openalex.org/W4408353170","https://openalex.org/W4408354521","https://openalex.org/W4415524661"],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"present":[4],"a":[5,124],"runtime-efficient":[6],"method":[7,27,98,122],"for":[8],"2D":[9],"pathloss":[10,82],"(PL)":[11],"map":[12],"prediction":[13],"in":[14,41,59,101],"complex":[15],"indoor":[16],"environments,":[17],"based":[18],"on":[19],"the":[20,51,60,74,95,102,120],"U-Net":[21],"convolutional":[22],"neural":[23],"network.":[24,96],"The":[25,107,115],"proposed":[26,121],"reconstructs":[28],"full":[29],"PL":[30],"maps":[31],"assisted":[32],"by":[33],"sparse":[34],"measurements":[35],"and":[36,83],"preprocessed":[37],"environment-aware":[38],"geometrical":[39],"features":[40,49,72,93],"highly-cluttered":[42],"environments.":[43],"We":[44],"empirically":[45],"show":[46],"that":[47,119],"such":[48],"help":[50],"network":[52],"not":[53],"only":[54],"generalize":[55],"to":[56,64,94],"unseen":[57],"points":[58],"same":[61],"environment":[62],"but":[63],"different":[65],"environments":[66],"as":[67,91],"well.":[68],"Some":[69],"of":[70,104,131,139],"these":[71],"include":[73],"obstruction":[75],"count":[76],"map,":[77,86],"accumulated":[78],"transmittance":[79],"maps,":[80],"free-space":[81],"log-scaled":[84],"distance":[85],"which":[87],"are":[88],"collectively":[89],"used":[90],"input":[92],"Our":[97],"is":[99],"evaluated":[100],"context":[103],"MLSP":[105],"2025":[106],"Sampling-Assisted":[108],"Pathloss":[109],"Radio":[110],"Map":[111],"Prediction":[112],"Data":[113],"Competition.":[114],"evaluation":[116],"results":[117],"demonstrate":[118],"achieves":[123],"weighted":[125],"final":[126],"root":[127],"mean":[128],"square":[129],"error":[130],"4.80":[132],"dB":[133],"with":[134],"an":[135],"average":[136],"total":[137],"runtime":[138],"14.36":[140],"milliseconds.":[141]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-24T00:00:00"}
