{"id":"https://openalex.org/W2982455162","doi":"https://doi.org/10.1109/wcnc.2019.8885668","title":"Path Loss Exponent Prediction for Outdoor Millimeter Wave Channels through Deep Learning","display_name":"Path Loss Exponent Prediction for Outdoor Millimeter Wave Channels through Deep Learning","publication_year":2019,"publication_date":"2019-04-01","ids":{"openalex":"https://openalex.org/W2982455162","doi":"https://doi.org/10.1109/wcnc.2019.8885668","mag":"2982455162"},"language":"en","primary_location":{"id":"doi:10.1109/wcnc.2019.8885668","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc.2019.8885668","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Wireless Communications and Networking Conference (WCNC)","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":"https://openalex.org/A5091400802","display_name":"JunG-Yong Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]},{"id":"https://openalex.org/I4210164379","display_name":"Seoul Media Institute of Technology","ror":"https://ror.org/04ywg4h07","country_code":"KR","type":"education","lineage":["https://openalex.org/I4210164379"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"JunG-Yong Lee","raw_affiliation_strings":["Institute of New Media and Communications, Seoul National University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Institute of New Media and Communications, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I4210164379","https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012425710","display_name":"Min Young Kang","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]},{"id":"https://openalex.org/I4210164379","display_name":"Seoul Media Institute of Technology","ror":"https://ror.org/04ywg4h07","country_code":"KR","type":"education","lineage":["https://openalex.org/I4210164379"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Min Young Kang","raw_affiliation_strings":["Institute of New Media and Communications, Seoul National University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Institute of New Media and Communications, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I4210164379","https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100745874","display_name":"Seong-Cheol Kim","orcid":"https://orcid.org/0000-0002-7896-5625"},"institutions":[{"id":"https://openalex.org/I4210164379","display_name":"Seoul Media Institute of Technology","ror":"https://ror.org/04ywg4h07","country_code":"KR","type":"education","lineage":["https://openalex.org/I4210164379"]},{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seong-Cheol Kim","raw_affiliation_strings":["Institute of New Media and Communications, Seoul National University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Institute of New Media and Communications, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I4210164379","https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5091400802"],"corresponding_institution_ids":["https://openalex.org/I139264467","https://openalex.org/I4210164379"],"apc_list":null,"apc_paid":null,"fwci":2.299,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.88880033,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"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":0.9997000098228455,"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":0.9997000098228455,"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":0.9725000262260437,"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":0.940500020980835,"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/exponent","display_name":"Exponent","score":0.8167250752449036},{"id":"https://openalex.org/keywords/path-loss","display_name":"Path loss","score":0.7690722942352295},{"id":"https://openalex.org/keywords/extremely-high-frequency","display_name":"Extremely high frequency","score":0.6417826414108276},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5862892270088196},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5758843421936035},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5748966336250305},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.5440506339073181},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5161753296852112},{"id":"https://openalex.org/keywords/transmitter","display_name":"Transmitter","score":0.46949437260627747},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4493940472602844},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4432351291179657},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3923780024051666},{"id":"https://openalex.org/keywords/topology","display_name":"Topology (electrical circuits)","score":0.37924301624298096},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27233853936195374},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.25679558515548706},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.1401975154876709},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.10171329975128174}],"concepts":[{"id":"https://openalex.org/C2780388253","wikidata":"https://www.wikidata.org/wiki/Q5421508","display_name":"Exponent","level":2,"score":0.8167250752449036},{"id":"https://openalex.org/C194273485","wikidata":"https://www.wikidata.org/wiki/Q1478845","display_name":"Path loss","level":3,"score":0.7690722942352295},{"id":"https://openalex.org/C45764600","wikidata":"https://www.wikidata.org/wiki/Q570342","display_name":"Extremely high frequency","level":2,"score":0.6417826414108276},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5862892270088196},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5758843421936035},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5748966336250305},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.5440506339073181},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5161753296852112},{"id":"https://openalex.org/C47798520","wikidata":"https://www.wikidata.org/wiki/Q190157","display_name":"Transmitter","level":3,"score":0.46949437260627747},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4493940472602844},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4432351291179657},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3923780024051666},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.37924301624298096},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27233853936195374},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.25679558515548706},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.1401975154876709},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.10171329975128174},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/wcnc.2019.8885668","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc.2019.8885668","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Wireless Communications and Networking Conference (WCNC)","raw_type":"proceedings-article"},{"id":"pmh:oai:s-space.snu.ac.kr:10371/186755","is_oa":false,"landing_page_url":"https://hdl.handle.net/10371/186755","pdf_url":null,"source":{"id":"https://openalex.org/S4306401345","display_name":"Seoul National University Open Repository (Seoul National University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I139264467","host_organization_name":"Seoul National University","host_organization_lineage":["https://openalex.org/I139264467"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.5899999737739563,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2047024541","https://openalex.org/W2128730550","https://openalex.org/W2584282241","https://openalex.org/W2745902057","https://openalex.org/W2751375634","https://openalex.org/W2788671996","https://openalex.org/W2790254411","https://openalex.org/W2793889311","https://openalex.org/W2807927039","https://openalex.org/W2903395273"],"related_works":["https://openalex.org/W2349806816","https://openalex.org/W2398398527","https://openalex.org/W3110590130","https://openalex.org/W4287562279","https://openalex.org/W2504274537","https://openalex.org/W2886074965","https://openalex.org/W2972536919","https://openalex.org/W2512234407","https://openalex.org/W905396345","https://openalex.org/W2521006054"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,80],"propose":[4],"a":[5,64],"new":[6],"algorithm":[7,25],"for":[8,90],"predicting":[9],"the":[10,27,43,53,74,77,82,85,98],"path":[11,59,69],"loss":[12,60,70],"exponent":[13],"of":[14,29,76,84],"outdoor":[15,54],"millimeter-wave":[16],"band":[17,56],"channels":[18],"through":[19],"deep":[20],"learning":[21],"method.":[22],"The":[23],"proposed":[24,78],"has":[26],"advantage":[28],"requiring":[30],"less":[31],"inference":[32],"time":[33],"compared":[34],"to":[35,51],"existing":[36],"deterministic":[37],"channel":[38,57],"models":[39],"while":[40],"concretely":[41],"considering":[42],"topographical":[44],"characteristics.":[45],"We":[46,62],"used":[47],"three-dimensional":[48],"ray":[49],"tracing":[50],"generate":[52],"millimeterwave":[55],"and":[58,87,94],"exponent.":[61,71],"trained":[63],"neural":[65],"network":[66],"with":[67],"generated":[68],"To":[72],"evaluate":[73],"performance":[75],"method,":[79],"analyzed":[81],"influence":[83],"hyperparameters":[86],"environmental":[88],"features,":[89],"example,":[91],"building":[92],"density":[93],"average":[95],"distance":[96],"from":[97],"transmitter.":[99]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":2}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}