{"id":"https://openalex.org/W7162755007","doi":"https://doi.org/10.1109/twc.2026.3696997","title":"Point-Cloud-Assistant Localized Statistical Channel Prediction by Tangent Gaussian Splatting","display_name":"Point-Cloud-Assistant Localized Statistical Channel Prediction by Tangent Gaussian Splatting","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7162755007","doi":"https://doi.org/10.1109/twc.2026.3696997"},"language":null,"primary_location":{"id":"doi:10.1109/twc.2026.3696997","is_oa":false,"landing_page_url":"https://doi.org/10.1109/twc.2026.3696997","pdf_url":null,"source":{"id":"https://openalex.org/S63459445","display_name":"IEEE Transactions on Wireless Communications","issn_l":"1536-1276","issn":["1536-1276","1558-2248"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Wireless Communications","raw_type":"journal-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/A5126738545","display_name":"Ye Xue","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ye Xue","raw_affiliation_strings":["School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-9629-8996","affiliations":[{"raw_affiliation_string":"School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961","https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100659813","display_name":"Yiheng Wang","orcid":"https://orcid.org/0000-0002-5185-3551"},"institutions":[{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiheng Wang","raw_affiliation_strings":["Shenzhen Research Institute of Big Data, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0006-5129-889X","affiliations":[{"raw_affiliation_string":"Shenzhen Research Institute of Big Data, Shenzhen, China","institution_ids":["https://openalex.org/I4210099586"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137346167","display_name":"Xinhua Shao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]},{"id":"https://openalex.org/I4387153335","display_name":"China Telecom","ror":"https://ror.org/05p67dv18","country_code":null,"type":"company","lineage":["https://openalex.org/I4387153335"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinhua Shao","raw_affiliation_strings":["China Telecom, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Telecom, Beijing, China","institution_ids":["https://openalex.org/I4210136246","https://openalex.org/I4387153335"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137327239","display_name":"Qi Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Yan","raw_affiliation_strings":["Networking and User Experience Laboratory, Huawei Technologies, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Networking and User Experience Laboratory, Huawei Technologies, Shenzhen, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055793509","display_name":"S. Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shutao Zhang","raw_affiliation_strings":["Networking and User Experience Laboratory, Huawei Technologies, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Networking and User Experience Laboratory, Huawei Technologies, Shenzhen, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064271996","display_name":"Tsung\u2010Hui Chang","orcid":"https://orcid.org/0000-0003-1349-2764"},"institutions":[{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tsung-Hui Chang","raw_affiliation_strings":["Shenzhen Research Institute of Big Data, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-1349-2764","affiliations":[{"raw_affiliation_string":"Shenzhen Research Institute of Big Data, Shenzhen, China","institution_ids":["https://openalex.org/I4210099586"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.66733761,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"25","issue":null,"first_page":"17816","last_page":"17830"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.15610000491142273,"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"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.15610000491142273,"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"}},{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.12489999830722809,"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"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.10419999808073044,"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":0.486299991607666},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.43779999017715454},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.37139999866485596},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.3684999942779541},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.3474000096321106},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.32510000467300415},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.3077000081539154}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6352999806404114},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5252000093460083},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.486299991607666},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.43779999017715454},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.37139999866485596},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.3684999942779541},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.3474000096321106},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.32510000467300415},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.3077000081539154},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29829999804496765},{"id":"https://openalex.org/C138187205","wikidata":"https://www.wikidata.org/wiki/Q131251","display_name":"Tangent","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C51267290","wikidata":"https://www.wikidata.org/wiki/Q5527848","display_name":"Gaussian random field","level":4,"score":0.2953999936580658},{"id":"https://openalex.org/C2986587452","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical analysis","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2865999937057495},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2750000059604645},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.2680000066757202},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2630000114440918}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/twc.2026.3696997","is_oa":false,"landing_page_url":"https://doi.org/10.1109/twc.2026.3696997","pdf_url":null,"source":{"id":"https://openalex.org/S63459445","display_name":"IEEE Transactions on Wireless Communications","issn_l":"1536-1276","issn":["1536-1276","1558-2248"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Wireless Communications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.46674469113349915}],"awards":[{"id":"https://openalex.org/G2247308448","display_name":null,"funder_award_id":"62031008","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5803679919","display_name":null,"funder_award_id":"62301334","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W2042696029","https://openalex.org/W2110784079","https://openalex.org/W2146000945","https://openalex.org/W2160063707","https://openalex.org/W2160691714","https://openalex.org/W2169030369","https://openalex.org/W2964121744","https://openalex.org/W3016619384","https://openalex.org/W3131967102","https://openalex.org/W3202699980","https://openalex.org/W4292027041","https://openalex.org/W4312325284","https://openalex.org/W4323897327","https://openalex.org/W4376167392","https://openalex.org/W4381162639","https://openalex.org/W4385318467","https://openalex.org/W4391827352","https://openalex.org/W4391941434","https://openalex.org/W4396712521","https://openalex.org/W4402754045","https://openalex.org/W4402775411","https://openalex.org/W4402816534","https://openalex.org/W4402915187","https://openalex.org/W4403989853","https://openalex.org/W4404035596","https://openalex.org/W4408357858","https://openalex.org/W4408711180","https://openalex.org/W4413967860","https://openalex.org/W7084144840"],"related_works":[],"abstract_inverted_index":{"Accurate,":[0],"site-specific":[1],"channel":[2,16,22,212],"information":[3],"is":[4],"crucial":[5],"for":[6,43,157,196],"optimizing":[7],"next-generation":[8],"wireless":[9,216],"networks.":[10],"Among":[11],"various":[12],"approaches,":[13],"localized":[14],"statistical":[15],"modeling":[17],"(LSCM),":[18],"which":[19,66],"models":[20],"the":[21,29,56,86,121,133,203],"multipath":[23],"angular":[24,135],"power":[25,33],"spectrum":[26],"(APS)":[27],"from":[28],"reference":[30],"signal":[31],"received":[32],"(RSRP)":[34],"measurement,":[35],"has":[36],"emerged":[37],"as":[38,109],"a":[39,117,138,152,163,169],"state-of-the-art":[40,190],"method":[41],"tailored":[42],"efficient":[44],"network":[45],"optimization.":[46],"However,":[47],"despite":[48],"its":[49,69],"effectiveness,":[50],"LSCM":[51],"cannot":[52],"predict":[53],"APS":[54,91,158,183,197],"at":[55],"vast":[57],"majority":[58],"of":[59,120,205],"locations":[60],"where":[61],"no":[62],"measurements":[63,100],"are":[64],"available,":[65],"significantly":[67],"restricts":[68],"applicability":[70],"in":[71,214],"large-scale,":[72],"real-world":[73],"scenarios.":[74],"To":[75,146],"address":[76],"this":[77],"challenge,":[78],"we":[79,150],"present":[80],"point-cloud-assisted":[81],"tangent":[82],"Gaussian":[83,131],"splatting":[84,141],"(PC-TGS),":[85],"first":[87],"framework":[88],"to":[89,92,189,207],"extrapolate":[90],"unmeasured":[93],"outdoor":[94],"grids":[95],"by":[96],"integrating":[97],"sparse":[98],"radio":[99],"with":[101],"dense":[102],"LiDAR-based":[103],"geometry.":[104],"PC-TGS":[105,180,206],"represents":[106],"environmental":[107],"scatterers":[108],"anisotropic":[110],"3D":[111],"Gaussians,":[112],"initialized":[113],"and":[114,161,184,192,210],"refined":[115],"through":[116],"relaxed-mean":[118],"reparaeterization":[119],"raw":[122],"point":[123],"cloud.":[124],"A":[125],"tangent-plane":[126],"projection":[127],"accurately":[128],"maps":[129],"each":[130],"into":[132],"local":[134],"domain,":[136],"while":[137],"depth-aware":[139],"electromagnetic":[140],"process":[142],"aggregates":[143],"their":[144],"contributions.":[145],"ensure":[147],"practical":[148],"deployment,":[149],"derive":[151],"closed-form":[153],"Gaussian-weighted":[154],"average":[155],"(GWA)":[156],"bin":[159],"integration":[160],"provide":[162],"provable":[164],"error":[165],"bound.":[166],"Evaluations":[167],"on":[168],"LiDAR-scanned":[170],"city-scale":[171],"dataset":[172],"(5M":[173],"points,":[174],"6,310":[175],"RSRP":[176,185],"samples)":[177],"demonstrate":[178],"that":[179],"achieves":[181],"better":[182],"prediction":[186,213],"performance":[187],"compared":[188],"baselines":[191],"faster":[193],"inference":[194],"time":[195],"extrapolation":[198],"task.":[199],"These":[200],"results":[201],"highlight":[202],"potential":[204],"enable":[208],"geometry-aware":[209],"data-efficient":[211],"large-scale":[215],"digital":[217],"twins.":[218]},"counts_by_year":[],"updated_date":"2026-05-31T06:10:14.724740","created_date":"2026-05-30T00:00:00"}