{"id":"https://openalex.org/W7162124191","doi":"https://doi.org/10.48550/arxiv.2605.21815","title":"Near-Field User Location Inference From Far-Field Power Measurements","display_name":"Near-Field User Location Inference From Far-Field Power Measurements","publication_year":2026,"publication_date":"2026-05-20","ids":{"openalex":"https://openalex.org/W7162124191","doi":"https://doi.org/10.48550/arxiv.2605.21815"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.21815","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21815","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.2605.21815","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136787238","display_name":"Shima Mashhadi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mashhadi, Shima","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014062090","display_name":"Tiep M. Hoang","orcid":"https://orcid.org/0000-0002-9806-6408"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hoang, Tiep M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5031294876","display_name":"Alireza Vahid","orcid":"https://orcid.org/0000-0002-5079-4617"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vahid, Alireza","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":0.2206999957561493,"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":0.2206999957561493,"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":0.15729999542236328,"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/T11392","display_name":"Energy Harvesting in Wireless Networks","score":0.15549999475479126,"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/inference","display_name":"Inference","score":0.6869999766349792},{"id":"https://openalex.org/keywords/multipath-propagation","display_name":"Multipath propagation","score":0.6047999858856201},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5932999849319458},{"id":"https://openalex.org/keywords/leakage","display_name":"Leakage (economics)","score":0.507099986076355},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.487199991941452},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4569000005722046},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.41260001063346863},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.41190001368522644},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.39320001006126404}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6869999766349792},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6183000206947327},{"id":"https://openalex.org/C161218011","wikidata":"https://www.wikidata.org/wiki/Q11827794","display_name":"Multipath propagation","level":3,"score":0.6047999858856201},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5932999849319458},{"id":"https://openalex.org/C2777042071","wikidata":"https://www.wikidata.org/wiki/Q6509304","display_name":"Leakage (economics)","level":2,"score":0.507099986076355},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.487199991941452},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4569000005722046},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4244000017642975},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.41260001063346863},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.41190001368522644},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40130001306533813},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.39320001006126404},{"id":"https://openalex.org/C2779363554","wikidata":"https://www.wikidata.org/wiki/Q5420835","display_name":"Expectation propagation","level":4,"score":0.38269999623298645},{"id":"https://openalex.org/C113407356","wikidata":"https://www.wikidata.org/wiki/Q1431214","display_name":"Spectral leakage","level":3,"score":0.37790000438690186},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3763999938964844},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.33970001339912415},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.31859999895095825},{"id":"https://openalex.org/C168110828","wikidata":"https://www.wikidata.org/wiki/Q1331626","display_name":"Spectral density","level":2,"score":0.31380000710487366},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.3124000132083893},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3100000023841858},{"id":"https://openalex.org/C150627866","wikidata":"https://www.wikidata.org/wiki/Q1076893","display_name":"Field of view","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C88871306","wikidata":"https://www.wikidata.org/wiki/Q7208287","display_name":"Point process","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C152948882","wikidata":"https://www.wikidata.org/wiki/Q4060686","display_name":"Belief propagation","level":3,"score":0.2793000042438507},{"id":"https://openalex.org/C2779433544","wikidata":"https://www.wikidata.org/wiki/Q1435226","display_name":"Focal point","level":3,"score":0.2639999985694885},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.26260000467300415},{"id":"https://openalex.org/C96608239","wikidata":"https://www.wikidata.org/wiki/Q1199823","display_name":"Statistical power","level":2,"score":0.257999986410141}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.21815","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21815","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.2605.21815","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21815","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":[{"id":"https://metadata.un.org/sdg/7","score":0.8900611996650696,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Near-field":[0],"beamfocusing":[1,21],"enabled":[2],"by":[3,73],"extremely":[4],"large-aperture":[5],"arrays":[6],"(ELAA)":[7],"is":[8,130],"a":[9,36,52,87,105],"promising":[10],"6G":[11],"technique":[12],"for":[13],"massive":[14],"connectivity":[15],"and":[16,109,122,138],"high":[17],"spectrum":[18],"efficiency.":[19],"While":[20],"concentrates":[22],"energy":[23],"at":[24],"an":[25,110],"intended":[26],"user,":[27],"the":[28,32,42,70,80,95],"radiated":[29],"field":[30],"outside":[31],"focal":[33],"point":[34],"exhibits":[35],"structured":[37],"leakage":[38,50],"that":[39,48,93,126],"varies":[40],"with":[41,132],"focal-point":[43],"coordinates.":[44],"This":[45],"paper":[46],"shows":[47],"this":[49,75,99],"enables":[51],"new":[53],"form":[54],"of":[55,98],"passive":[56],"user":[57],"localization":[58],"in":[59],"which":[60],"distributed":[61],"far-field":[62],"sensors":[63,137],"measuring":[64],"only":[65],"received":[66],"power":[67,77],"can":[68],"infer":[69],"user's":[71],"location":[72,128],"exploiting":[74],"location-dependent":[76],"signature.":[78],"Using":[79],"induced":[81],"noncentral":[82],"chi-square":[83],"statistics,":[84],"we":[85],"derive":[86],"Bayesian":[88],"Cram\u00e9r-Rao":[89],"lower":[90],"bound":[91],"(BCRLB)":[92],"establishes":[94],"fundamental":[96],"limits":[97],"inference":[100,129],"problem.":[101],"We":[102],"then":[103],"evaluate":[104],"model-based":[106],"grid-search":[107],"estimator":[108],"attention-based":[111],"permutation-invariant":[112],"deep":[113],"learning":[114],"regressor":[115],"(DeepSet).":[116],"Results":[117],"under":[118],"both":[119],"line-of-sight":[120],"(LoS)":[121],"multipath":[123],"propagation":[124],"confirm":[125],"reliable":[127],"feasible,":[131],"accuracy":[133],"improving":[134],"as":[135],"more":[136],"snapshots":[139],"are":[140],"used.":[141]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-23T00:00:00"}