{"id":"https://openalex.org/W7154484455","doi":"https://doi.org/10.48550/arxiv.2604.11983","title":"A Geometric Algebra-informed NeRF Framework for Generalizable Wireless Channel Prediction","display_name":"A Geometric Algebra-informed NeRF Framework for Generalizable Wireless Channel Prediction","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154484455","doi":"https://doi.org/10.48550/arxiv.2604.11983"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.11983","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11983","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.2604.11983","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111498759","display_name":"J. T. Shen","orcid":"https://orcid.org/0009-0004-0055-2160"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Jingzhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133721363","display_name":"Luis Lago Enamorado","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Enamorado, Luis Lago","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133674762","display_name":"Shiwen Mao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mao, Shiwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5043788836","display_name":"Xuyu Wang","orcid":"https://orcid.org/0000-0002-4759-8674"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xuyu","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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.7333999872207642,"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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.7333999872207642,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.03519999980926514,"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":0.02669999934732914,"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/wireless","display_name":"Wireless","score":0.7114999890327454},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5947999954223633},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.527400016784668},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.45089998841285706},{"id":"https://openalex.org/keywords/tracing","display_name":"Tracing","score":0.4505000114440918},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.3959999978542328}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7609000205993652},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.7114999890327454},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5947999954223633},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.527400016784668},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4607999920845032},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.45089998841285706},{"id":"https://openalex.org/C138673069","wikidata":"https://www.wikidata.org/wiki/Q322229","display_name":"Tracing","level":2,"score":0.4505000114440918},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.3959999978542328},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.34049999713897705},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.33570000529289246},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3249000012874603},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27970001101493835},{"id":"https://openalex.org/C40319758","wikidata":"https://www.wikidata.org/wiki/Q5535477","display_name":"Geometric design","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.273499995470047},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2612000107765198}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.11983","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11983","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.2604.11983","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11983","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1,73],"paper,":[2],"we":[3,138],"propose":[4],"the":[5],"geometric":[6,21,108],"algebra-informed":[7],"neural":[8,112],"radiance":[9],"fields":[10],"(GAI-NeRF),":[11],"a":[12,76,116],"novel":[13],"framework":[14],"for":[15,119,153,172],"wireless":[16,88,121,132,173],"channel":[17,103,174],"prediction":[18,104],"that":[19,67,97],"leverages":[20],"algebra":[22,109],"attention":[23],"mechanisms":[24],"to":[25,49],"capture":[26],"ray-object":[27],"interactions":[28],"in":[29,44,61,102,164],"complex":[30],"propagation":[31],"environments.":[32],"Our":[33],"approach":[34,142],"incorporates":[35],"global":[36],"token":[37],"representations,":[38,114],"drawing":[39],"inspiration":[40],"from":[41],"transformer":[42],"architectures":[43],"language":[45],"and":[46,54,71,157,167],"vision":[47],"domains,":[48],"aggregate":[50],"learned":[51],"spatial-electromagnetic":[52],"features":[53],"enhance":[55],"scene":[56,113],"understanding.":[57],"We":[58],"identify":[59],"limitations":[60],"conventional":[62],"static":[63],"ray":[64,78],"tracing":[65,79],"modules":[66],"hinder":[68],"model":[69],"generalization":[70,85,158],"address":[72],"challenge":[74],"through":[75],"new":[77],"architecture.":[80],"This":[81],"design":[82],"enables":[83],"effective":[84],"across":[86,130],"diverse":[87],"scenarios":[89],"while":[90],"maintaining":[91],"computational":[92],"efficiency.":[93],"Experimental":[94],"results":[95],"demonstrate":[96],"GAI-NeRF":[98,125],"achieves":[99],"superior":[100],"performance":[101,163],"tasks":[105,156],"by":[106],"combining":[107],"principles":[110],"with":[111],"offering":[115],"promising":[117],"direction":[118],"next-generation":[120],"communication":[122],"systems.":[123],"Moreover,":[124],"greatly":[126],"outperforms":[127],"existing":[128],"methods":[129],"multiple":[131,144],"scenarios.":[133],"To":[134],"ensure":[135],"comprehensive":[136],"assessment,":[137],"further":[139],"evaluate":[140],"our":[141],"against":[143],"benchmarks":[145],"using":[146],"newly":[147],"collected":[148],"real-world":[149],"indoor":[150],"datasets":[151],"tailored":[152],"single-scene":[154],"downstream":[155],"testing,":[159],"confirming":[160],"its":[161,169],"robust":[162],"unseen":[165],"environments":[166],"establishing":[168],"high":[170],"efficacy":[171],"prediction.":[175]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-16T00:00:00"}
