{"id":"https://openalex.org/W7147201402","doi":"https://doi.org/10.1109/icaiic68212.2026.11454241","title":"Data-Driven Path Loss Modeling Using Multilayer Perceptron Networks","display_name":"Data-Driven Path Loss Modeling Using Multilayer Perceptron Networks","publication_year":2026,"publication_date":"2026-02-24","ids":{"openalex":"https://openalex.org/W7147201402","doi":"https://doi.org/10.1109/icaiic68212.2026.11454241"},"language":"en","primary_location":{"id":"doi:10.1109/icaiic68212.2026.11454241","doi":"https://doi.org/10.1109/icaiic68212.2026.11454241","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaiic68212.2026.11454241","pdf_url":null,"source":{"id":"https://openalex.org/S7407088616","display_name":"International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5064791621","display_name":"Dong-seok Lee","orcid":"https://orcid.org/0009-0006-7069-8856","observed_orcids":["https://orcid.org/0009-0006-7069-8856"]},"institutions":[{"id":"https://openalex.org/I4575257","display_name":"Hanyang University","ror":"https://ror.org/046865y68","country_code":"KR","type":"education","lineage":["https://openalex.org/I4575257"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dong-Seok Lee","raw_affiliation_strings":["Hanyang University,dept. Automotive Engineering (Automotive-Computer Convergence),Seoul,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hanyang University,dept. Automotive Engineering (Automotive-Computer Convergence),Seoul,Republic of Korea","institution_ids":["https://openalex.org/I4575257"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048331785","display_name":"Taesik Nam","orcid":"https://orcid.org/0000-0002-6113-2475","observed_orcids":["https://orcid.org/0000-0002-6113-2475","https://orcid.org/0009-0005-4144-6935"]},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Taesik Nam","raw_affiliation_strings":["Yonsei University,dept. Electrical and Electronic Engineering,Seoul,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yonsei University,dept. Electrical and Electronic Engineering,Seoul,Republic of Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020092651","display_name":"Han\u2010Shin Jo","orcid":"https://orcid.org/0000-0002-5738-1807","observed_orcids":["https://orcid.org/0000-0002-5738-1807"]},"institutions":[{"id":"https://openalex.org/I4575257","display_name":"Hanyang University","ror":"https://ror.org/046865y68","country_code":"KR","type":"education","lineage":["https://openalex.org/I4575257"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Han-Shin Jo","raw_affiliation_strings":["Hanyang University,dept. Automotive Engineering,Seoul,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hanyang University,dept. Automotive Engineering,Seoul,Republic of Korea","institution_ids":["https://openalex.org/I4575257"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"184","last_page":"186"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.9970999956130981,"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":0.9970999956130981,"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":0.00019999999494757503,"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/T11409","display_name":"Advanced Wireless Network Optimization","score":0.00019999999494757503,"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/path-loss-prediction","display_name":"path loss prediction","score":0.9539999961853027},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"multilayer perceptron","score":0.8619999885559082},{"id":"https://openalex.org/keywords/support-vector-regression","display_name":"support vector regression","score":0.9779999852180481},{"id":"https://openalex.org/keywords/multivariable-linear-regression","display_name":"multivariable linear regression","score":0.972000002861023},{"id":"https://openalex.org/keywords/nonlinear-propagation","display_name":"nonlinear propagation","score":0.8180000185966492},{"id":"https://openalex.org/keywords/wireless-channel-modeling","display_name":"wireless channel modeling","score":0.7609999775886536},{"id":"https://openalex.org/keywords/model-generalization","display_name":"model generalization","score":0.6019999980926514}],"concepts":[{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.6536999940872192},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.6518999934196472},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6362000107765198},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5681999921798706},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5493999719619751},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.527400016784668},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5264999866485596},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.521399974822998},{"id":"https://openalex.org/C194273485","wikidata":"https://www.wikidata.org/wiki/Q1478845","display_name":"Path loss","level":3,"score":0.45660001039505005},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.4415999948978424},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.43630000948905945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4359999895095825},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3993000090122223},{"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/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3856000006198883},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3856000006198883},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3546000123023987},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.3452000021934509},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34040001034736633},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3382999897003174},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3346000015735626},{"id":"https://openalex.org/C117312493","wikidata":"https://www.wikidata.org/wiki/Q2035437","display_name":"Multivariable calculus","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2955999970436096},{"id":"https://openalex.org/C46889948","wikidata":"https://www.wikidata.org/wiki/Q2755024","display_name":"Nonlinear regression","level":3,"score":0.29429998993873596},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2939999997615814},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.29159998893737793},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C131109320","wikidata":"https://www.wikidata.org/wiki/Q581012","display_name":"Linear prediction","level":2,"score":0.2630000114440918}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icaiic68212.2026.11454241","doi":"https://doi.org/10.1109/icaiic68212.2026.11454241","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaiic68212.2026.11454241","pdf_url":null,"source":{"id":"https://openalex.org/S7407088616","display_name":"International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":1,"referenced_works":["https://openalex.org/W2145964855"],"related_works":["https://openalex.org/W2026011980","https://openalex.org/W3175909665","https://openalex.org/W4288095970","https://openalex.org/W7217737089","https://openalex.org/W7161488639","https://openalex.org/W4402363865","https://openalex.org/W4281899332","https://openalex.org/W3046498183","https://openalex.org/W4285122222","https://openalex.org/W1995813985"],"abstract_inverted_index":{"This":[0],"study":[1],"compares":[2],"ML/DL-based":[3,146],"path":[4,151],"loss":[5,152],"prediction":[6,134,153],"models":[7,73],"using":[8,34],"empirically":[9],"measured":[10],"data":[11],"while":[12],"accounting":[13],"for":[14],"regional":[15],"nonlinear":[16,107,147],"propagation":[17,122],"characteristics.":[18],"Multivariable":[19],"Linear":[20],"Regression":[21,25],"(MLR),":[22],"Support":[23],"Vector":[24],"(SVR),":[26],"and":[27,39,41,52,87,91],"a":[28],"Multi-Layer":[29],"Perceptron":[30],"(MLP)":[31],"were":[32],"trained":[33],"log-transformed":[35],"representations":[36],"of":[37,70,115,145],"distance":[38],"frequency,":[40],"their":[42],"generalization":[43],"performance":[44,69],"was":[45,74,83,102],"evaluated":[46,138],"in":[47,60,78,110,155],"two":[48],"regions":[49],"(Area":[50],"A":[51],"Area":[53,61,79,116,119],"B).":[54],"The":[55],"experimental":[56],"results":[57],"revealed":[58],"that":[59],"A,":[62],"characterized":[63],"by":[64],"strong":[65],"linearity,":[66],"the":[67,71,99,111,132,137,143],"predictive":[68],"three":[72],"generally":[75],"comparable;":[76],"however,":[77],"B,":[80,120],"where":[81,121],"nonlinearity":[82],"pronounced,":[84],"both":[85],"SVR":[86],"MLP":[88,100],"exhibited":[89],"higher$R^{2}$values":[90],"lower":[92],"RMSE":[93],"compared":[94],"to":[95,104,127,149],"MLR.":[96],"In":[97,118],"particular,":[98],"model":[101],"able":[103],"capture":[105],"small":[106],"variations":[108],"even":[109],"mostly":[112],"linear":[113],"characteristics":[114],"A.":[117],"fluctuates":[123],"more":[124],"severely":[125],"due":[126],"environmental":[128],"factors,":[129],"it":[130],"achieved":[131],"highest":[133],"accuracy":[135,154],"among":[136],"models.":[139],"These":[140],"findings":[141],"highlight":[142],"potential":[144],"approaches":[148],"improve":[150],"diverse":[156],"wireless":[157],"channels.":[158]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-04-02T00:00:00"}