{"id":"https://openalex.org/W7162446838","doi":"https://doi.org/10.48550/arxiv.2605.24028","title":"Radio Environment Mapping with World Models for Active Measurement Control: Should Networks Dream of Optimal Control?","display_name":"Radio Environment Mapping with World Models for Active Measurement Control: Should Networks Dream of Optimal Control?","publication_year":2026,"publication_date":"2026-05-20","ids":{"openalex":"https://openalex.org/W7162446838","doi":"https://doi.org/10.48550/arxiv.2605.24028"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.24028","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24028","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.24028","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054993601","display_name":"Jernej Hribar","orcid":"https://orcid.org/0000-0002-9446-7917"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hribar, Jernej","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039327723","display_name":"Ljupcho Milosheski","orcid":"https://orcid.org/0000-0003-1087-3502"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Milosheski, Ljupcho","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5087831856","display_name":"Ryoichi Shinkuma","orcid":"https://orcid.org/0000-0003-2842-8941"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shinkuma, Ryoichi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":0.30979999899864197,"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":0.30979999899864197,"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":0.2793999910354614,"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/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.062300000339746475,"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/interpolation","display_name":"Interpolation (computer graphics)","score":0.6402999758720398},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4521999955177307},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.41530001163482666},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.3903000056743622},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.38260000944137573},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.3714999854564667},{"id":"https://openalex.org/keywords/measurement-uncertainty","display_name":"Measurement uncertainty","score":0.34950000047683716},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3490000069141388}],"concepts":[{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.6402999758720398},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6345000267028809},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4521999955177307},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4235999882221222},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41780000925064087},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.41530001163482666},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3903000056743622},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.38589999079704285},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.38260000944137573},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3714999854564667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.350600004196167},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.34950000047683716},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.3163999915122986},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C202311505","wikidata":"https://www.wikidata.org/wiki/Q1474701","display_name":"Radio propagation","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2709999978542328},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C22607594","wikidata":"https://www.wikidata.org/wiki/Q5375150","display_name":"Enabling","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C557945733","wikidata":"https://www.wikidata.org/wiki/Q389772","display_name":"Data transmission","level":2,"score":0.2526000142097473}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.24028","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24028","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.24028","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24028","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.41287270188331604,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Radio":[0],"Environment":[1],"Maps":[2],"(REMs)":[3],"have":[4],"the":[5,91,101,106,126,135,151,159],"potential":[6,160],"to":[7,44,99,140],"serve":[8],"as":[9,66,164],"an":[10,41,87],"important":[11],"enabler":[12],"for":[13,76,168],"intelligent":[14,174],"modeling":[15],"and":[16,39,71,94,173,179],"control":[17],"in":[18,134,144,177],"emerging":[19],"AI-native":[20],"6G":[21,178],"networks.":[22,181],"Despite":[23],"significant":[24],"progress,":[25],"most":[26],"REM":[27,64],"construction":[28,65],"methods":[29],"remain":[30],"passive,":[31],"relying":[32],"on":[33,119],"interpolation":[34,133],"or":[35],"static":[36],"uncertainty":[37],"models":[38,163],"lacking":[40],"explicit":[42],"mechanism":[43,98],"reason":[45],"about":[46],"how":[47],"future":[48],"measurements":[49],"will":[50],"affect":[51],"reconstruction":[52],"quality":[53],"under":[54,113],"a":[55,67,73,96,114,141,165],"limited":[56,115],"measurement":[57,111],"budget.":[58,116],"In":[59],"this":[60],"paper,":[61],"we":[62],"formulate":[63],"sequential":[68],"decision-making":[69],"problem":[70],"propose":[72],"world-model-inspired":[74],"framework":[75],"active":[77],"Received":[78],"Signal":[79],"Strength":[80],"Indicator":[81],"(RSSI)":[82],"map":[83],"reconstruction.":[84],"By":[85],"learning":[86],"internal":[88],"representation":[89],"of":[90,103,154,161],"radio":[92,170],"environment":[93,171],"employing":[95],"dreaming":[97],"simulate":[100],"impact":[102],"candidate":[104],"measurements,":[105],"proposed":[107,127],"approach":[108],"actively":[109],"selects":[110],"locations":[112],"Experimental":[117],"results":[118,157],"real":[120],"indoor":[121],"RSSI":[122],"data":[123],"demonstrate":[124],"that":[125],"method":[128],"significantly":[129],"outperforms":[130],"Gaussian":[131],"Process-based":[132],"few-shot":[136],"regime,":[137],"achieving":[138],"up":[139],"fivefold":[142],"reduction":[143],"Root":[145],"Mean":[146],"Square":[147],"Error":[148],"(RMSE)":[149],"with":[150],"same":[152],"number":[153],"measurements.":[155],"These":[156],"highlight":[158],"world":[162],"powerful":[166],"paradigm":[167],"sample-efficient":[169],"mapping":[172],"model-based":[175],"sensing":[176],"beyond":[180]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-27T00:00:00"}
