{"id":"https://openalex.org/W7140804312","doi":"https://doi.org/10.48550/arxiv.2603.23787","title":"Digital Twin-Assisted Measurement Design and Channel Statistics Prediction","display_name":"Digital Twin-Assisted Measurement Design and Channel Statistics Prediction","publication_year":2026,"publication_date":"2026-03-24","ids":{"openalex":"https://openalex.org/W7140804312","doi":"https://doi.org/10.48550/arxiv.2603.23787"},"language":"en","primary_location":{"id":"pmh:oai:pure.atira.dk:openaire/3a57918f-4379-420b-83a0-c9e4a7136efe","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/3a57918f-4379-420b-83a0-c9e4a7136efe","pdf_url":null,"source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Williams, R J, Abouamer, M S & Popovski, P 2026 'Digital Twin-Assisted Measurement Design and Channel Statistics Prediction' arXiv. https://doi.org/10.48550/arXiv.2603.23787","raw_type":"workingPaper"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://vbn.aau.dk/da/publications/3a57918f-4379-420b-83a0-c9e4a7136efe","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010408151","display_name":"Robin Jess Williams","orcid":"https://orcid.org/0000-0003-2730-5966"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Williams, Robin J.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054950082","display_name":"Mahmoud Saad Abouamer","orcid":"https://orcid.org/0000-0001-5941-269X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abouamer, Mahmoud Saad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126230387","display_name":"Petar Popovski","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Popovski, Petar","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/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.07819999754428864,"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/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.07819999754428864,"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":0.0746999979019165,"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.07199999690055847,"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/channel","display_name":"Channel (broadcasting)","score":0.5683000087738037},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.5598000288009644},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5321999788284302},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.47699999809265137},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4526999890804291},{"id":"https://openalex.org/keywords/imperfect","display_name":"Imperfect","score":0.4487999975681305},{"id":"https://openalex.org/keywords/wireless-network","display_name":"Wireless network","score":0.36320000886917114},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.35760000348091125},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3564999997615814}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6687999963760376},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5683000087738037},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.5598000288009644},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5321999788284302},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.47699999809265137},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4526999890804291},{"id":"https://openalex.org/C2780310539","wikidata":"https://www.wikidata.org/wiki/Q12547192","display_name":"Imperfect","level":2,"score":0.4487999975681305},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37049999833106995},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.36320000886917114},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.35760000348091125},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3564999997615814},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3237000107765198},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.3199000060558319},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31850001215934753},{"id":"https://openalex.org/C96608239","wikidata":"https://www.wikidata.org/wiki/Q1199823","display_name":"Statistical power","level":2,"score":0.31679999828338623},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.30660000443458557},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.295199990272522},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C119340705","wikidata":"https://www.wikidata.org/wiki/Q1628597","display_name":"Analysis of covariance","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2709999978542328},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C67226441","wikidata":"https://www.wikidata.org/wiki/Q1665389","display_name":"Robust statistics","level":3,"score":0.2590999901294708},{"id":"https://openalex.org/C2776250087","wikidata":"https://www.wikidata.org/wiki/Q16938869","display_name":"Power delay profile","level":5,"score":0.25369998812675476}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:pure.atira.dk:openaire/3a57918f-4379-420b-83a0-c9e4a7136efe","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/3a57918f-4379-420b-83a0-c9e4a7136efe","pdf_url":null,"source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Williams, R J, Abouamer, M S & Popovski, P 2026 'Digital Twin-Assisted Measurement Design and Channel Statistics Prediction' arXiv. https://doi.org/10.48550/arXiv.2603.23787","raw_type":"workingPaper"},{"id":"doi:10.48550/arxiv.2603.23787","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23787","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":"pmh:oai:pure.atira.dk:openaire/3a57918f-4379-420b-83a0-c9e4a7136efe","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/3a57918f-4379-420b-83a0-c9e4a7136efe","pdf_url":null,"source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Williams, R J, Abouamer, M S & Popovski, P 2026 'Digital Twin-Assisted Measurement Design and Channel Statistics Prediction' arXiv. https://doi.org/10.48550/arXiv.2603.23787","raw_type":"workingPaper"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Prediction":[0],"of":[1,38,57,112,118,150],"wireless":[2,16,58,172],"channels":[3],"and":[4,40,76,165],"their":[5,31],"statistics":[6,120],"is":[7],"a":[8,82,109,167],"fundamental":[9],"procedure":[10],"for":[11,100,170],"ensuring":[12],"performance":[13,32],"guarantees":[14],"in":[15],"systems.":[17],"Statistical":[18],"radio":[19],"maps":[20,94],"powered":[21],"by":[22,139],"Gaussian":[23],"processes":[24],"(GPs)":[25],"offer":[26],"flexible,":[27],"non-parametric":[28],"frameworks,":[29],"but":[30],"depends":[33],"critically":[34],"on":[35],"the":[36,122,127,133,156],"choice":[37],"mean":[39],"covariance":[41],"functions.":[42],"These":[43,103],"are":[44,106],"typically":[45],"learned":[46],"from":[47,92],"dense":[48],"measurements":[49],"without":[50],"exploiting":[51,126],"environmental":[52],"geometry.":[53],"Digital":[54],"twins":[55],"(DTs)":[56],"environments":[59],"leverage":[60],"computational":[61],"power":[62],"to":[63,72,95,131],"incorporate":[64],"geometric":[65],"information;":[66],"however,":[67],"they":[68],"require":[69],"costly":[70],"calibration":[71],"accurately":[73],"capture":[74],"material":[75],"propagation":[77],"characteristics.":[78],"This":[79],"work":[80],"introduces":[81],"hybrid":[83],"channel":[84,113,119,173],"prediction":[85,117,163],"framework":[86,134],"that":[87],"leverages":[88],"uncalibrated":[89],"DTs":[90,152],"derived":[91],"open-source":[93],"extract":[96],"geometry-induced":[97],"prior":[98],"information":[99],"GP":[101],"prediction.":[102,174],"structural":[104],"priors":[105],"fused":[107],"with":[108,153],"small":[110],"number":[111],"measurements,":[114],"enabling":[115],"data-efficient":[116],"across":[121],"entire":[123],"environment.":[124],"By":[125],"uncertainty":[128],"quantification":[129],"inherent":[130],"GPs,":[132],"supports":[135],"principled":[136],"measurement":[137,160],"selection":[138],"identifying":[140],"informative":[141],"probing":[142],"locations":[143],"under":[144],"resource":[145],"constraints.":[146],"Through":[147],"this":[148],"integration":[149],"imperfect":[151],"statistical":[154],"learning,":[155],"proposed":[157],"method":[158],"reduces":[159],"overhead,":[161],"improves":[162],"accuracy,":[164],"establishes":[166],"practical":[168],"approach":[169],"resource-efficient":[171]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2026-03-27T00:00:00"}