{"id":"https://openalex.org/W4414140611","doi":"https://doi.org/10.1109/apwcs67981.2025.11151884","title":"Evaluating Data Augmentation Approaches of Terrain Features for Deep Learning-Based Path Loss Prediction in 28 GHz Urban Environments","display_name":"Evaluating Data Augmentation Approaches of Terrain Features for Deep Learning-Based Path Loss Prediction in 28 GHz Urban Environments","publication_year":2025,"publication_date":"2025-08-20","ids":{"openalex":"https://openalex.org/W4414140611","doi":"https://doi.org/10.1109/apwcs67981.2025.11151884"},"language":"en","primary_location":{"id":"doi:10.1109/apwcs67981.2025.11151884","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apwcs67981.2025.11151884","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS)","raw_type":"proceedings-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":null,"display_name":"Ryugo Sato","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Ryugo Sato","raw_affiliation_strings":["University of Tokyo,Graduate School of Engineering,Japan"],"affiliations":[{"raw_affiliation_string":"University of Tokyo,Graduate School of Engineering,Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092969406","display_name":"So Ikoma","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"So Ikoma","raw_affiliation_strings":["University of Tokyo,Graduate School of Engineering,Japan"],"affiliations":[{"raw_affiliation_string":"University of Tokyo,Graduate School of Engineering,Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108324297","display_name":"Zhengdong Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Zhengdong Lin","raw_affiliation_strings":["University of Tokyo,Graduate School of Engineering,Japan"],"affiliations":[{"raw_affiliation_string":"University of Tokyo,Graduate School of Engineering,Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032900761","display_name":"Akira Ebihara","orcid":"https://orcid.org/0009-0003-1264-6482"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Akira Ebihara","raw_affiliation_strings":["University of Tokyo,Graduate School of Engineering,Japan"],"affiliations":[{"raw_affiliation_string":"University of Tokyo,Graduate School of Engineering,Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Sora Miyazawa","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Sora Miyazawa","raw_affiliation_strings":["University of Tokyo,Graduate School of Engineering,Japan"],"affiliations":[{"raw_affiliation_string":"University of Tokyo,Graduate School of Engineering,Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Weihan Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Weihan Fan","raw_affiliation_strings":["University of Tokyo,Graduate School of Engineering,Japan"],"affiliations":[{"raw_affiliation_string":"University of Tokyo,Graduate School of Engineering,Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087234322","display_name":"Hiroyuki Morikawa","orcid":"https://orcid.org/0000-0001-8628-3109"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroyuki Morikawa","raw_affiliation_strings":["University of Tokyo,Graduate School of Engineering,Japan"],"affiliations":[{"raw_affiliation_string":"University of Tokyo,Graduate School of Engineering,Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068346224","display_name":"Yoshiaki Narusue","orcid":"https://orcid.org/0000-0001-7957-0839"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshiaki Narusue","raw_affiliation_strings":["University of Tokyo,Graduate School of Engineering,Japan"],"affiliations":[{"raw_affiliation_string":"University of Tokyo,Graduate School of Engineering,Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22431918,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.9998999834060669,"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":0.9998999834060669,"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/T13905","display_name":"Telecommunications and Broadcasting Technologies","score":0.9858999848365784,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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":0.9851999878883362,"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/terrain","display_name":"Terrain","score":0.8108000159263611},{"id":"https://openalex.org/keywords/swap","display_name":"Swap (finance)","score":0.6711000204086304},{"id":"https://openalex.org/keywords/path-loss","display_name":"Path loss","score":0.6365000009536743},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.49239999055862427},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4253000020980835},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4009999930858612},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.38920000195503235},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.3711000084877014}],"concepts":[{"id":"https://openalex.org/C161840515","wikidata":"https://www.wikidata.org/wiki/Q186131","display_name":"Terrain","level":2,"score":0.8108000159263611},{"id":"https://openalex.org/C99821215","wikidata":"https://www.wikidata.org/wiki/Q1136583","display_name":"Swap (finance)","level":2,"score":0.6711000204086304},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6484000086784363},{"id":"https://openalex.org/C194273485","wikidata":"https://www.wikidata.org/wiki/Q1478845","display_name":"Path loss","level":3,"score":0.6365000009536743},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.49239999055862427},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46630001068115234},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4253000020980835},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4009999930858612},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.38920000195503235},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38190001249313354},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.37459999322891235},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3515999913215637},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3424000144004822},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3361999988555908},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.3142000138759613},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.30379998683929443},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.30079999566078186},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C99695388","wikidata":"https://www.wikidata.org/wiki/Q350514","display_name":"Raised-relief map","level":3,"score":0.2791999876499176},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.2554999887943268}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apwcs67981.2025.11151884","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apwcs67981.2025.11151884","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2911643182","https://openalex.org/W3000408141","https://openalex.org/W3186604907","https://openalex.org/W4212889502","https://openalex.org/W4293057284","https://openalex.org/W4360604743","https://openalex.org/W4385804812","https://openalex.org/W4402156574","https://openalex.org/W4402157362","https://openalex.org/W4402978442","https://openalex.org/W4404035612"],"related_works":["https://openalex.org/W4399671601","https://openalex.org/W1992962589","https://openalex.org/W3032871857","https://openalex.org/W1743191351","https://openalex.org/W3104633800","https://openalex.org/W2069184433","https://openalex.org/W3023567978","https://openalex.org/W3044778482","https://openalex.org/W2914059119","https://openalex.org/W4380075502"],"abstract_inverted_index":{"Site-specific":[0],"path":[1,58,147],"loss":[2,59,148],"prediction":[3,18,96,149],"is":[4],"crucial":[5],"for":[6,175],"Beyond":[7],"5G/6G":[8],"systems.":[9],"Deep":[10],"learning":[11],"(DL)":[12],"has":[13],"attracted":[14],"attention":[15],"as":[16],"a":[17,62,72],"method":[19],"that":[20,92,142],"utilizes":[21],"detailed":[22],"terrain":[23,39],"information,":[24],"but":[25],"it":[26],"can":[27],"suffer":[28],"from":[29,71],"insufficient":[30],"data":[31,40,70,130],"volume":[32],"and":[33,48,112,136],"diversity.":[34],"This":[35,166],"paper":[36],"evaluates":[37],"two":[38],"augmentation":[41],"(DA)":[42],"techniques\u2014Tx-Rx":[43],"Swap":[44,50],"(TR-Swap),":[45],"leveraging":[46],"reciprocity,":[47],"Left-Right":[49],"(LR-Swap),":[51],"based":[52],"on":[53,66,83],"spatial":[54,153],"symmetry\u2014to":[55],"enhance":[56],"DL-based":[57,146],"prediction.":[60],"Using":[61],"CNN-BiLSTM":[63],"model":[64,177],"trained":[65],"28":[67],"GHz":[68],"ray-tracing":[69],"3D":[73],"Tokyo":[74],"model,":[75],"we":[76],"assessed":[77],"these":[78],"methods":[79],"via":[80],"4-fold":[81],"cross-validation":[82],"three":[84],"datasets":[85],"of":[86,171],"different":[87],"scales.":[88],"The":[89],"evaluation":[90],"revealed":[91],"LR-Swap":[93,143],"significantly":[94],"improved":[95],"accuracy,":[97],"reducing":[98],"root":[99],"mean":[100],"square":[101],"error":[102],"(RMSE)":[103],"by":[104,113,150],"approximately":[105,114],"1.0":[106],"dB":[107,116],"with":[108,117,163],"the":[109,118,133,156,168],"smaller-scale":[110],"dataset":[111],"0.6":[115],"larger-scale":[119],"dataset.":[120],"TR-Swap,":[121],"however,":[122],"showed":[123],"limited":[124],"benefit,":[125],"likely":[126],"due":[127],"to":[128],"generated":[129],"characteristics":[131],"mismatching":[132],"original":[134],"training":[135],"test":[137],"distributions.":[138],"These":[139],"findings":[140],"demonstrate":[141],"effectively":[144],"enhances":[145],"diversifying":[151],"learned":[152],"features":[154],"along":[155],"path,":[157],"proving":[158],"particularly":[159],"impactful":[160],"when":[161],"dealing":[162],"scarce":[164],"datasets.":[165],"highlights":[167],"significant":[169],"potential":[170],"well-chosen":[172],"DA":[173],"strategies":[174],"improving":[176],"performance":[178],"in":[179],"this":[180],"domain.":[181]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}