{"id":"https://openalex.org/W4313525020","doi":"https://doi.org/10.1109/latincom56090.2022.10000523","title":"Path-Loss Prediction of Millimeter-wave using Machine Learning Techniques","display_name":"Path-Loss Prediction of Millimeter-wave using Machine Learning Techniques","publication_year":2022,"publication_date":"2022-11-30","ids":{"openalex":"https://openalex.org/W4313525020","doi":"https://doi.org/10.1109/latincom56090.2022.10000523"},"language":"en","primary_location":{"id":"doi:10.1109/latincom56090.2022.10000523","is_oa":false,"landing_page_url":"https://doi.org/10.1109/latincom56090.2022.10000523","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","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/A5019652782","display_name":"Yoiz Nu\u00f1ez","orcid":"https://orcid.org/0000-0002-4694-2306"},"institutions":[{"id":"https://openalex.org/I2699952","display_name":"Pontifical Catholic University of Rio de Janeiro","ror":"https://ror.org/01dg47b60","country_code":"BR","type":"education","lineage":["https://openalex.org/I2699952"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Yoiz Nunez","raw_affiliation_strings":["Center of Study in Telecommunications, PUC-Rio,Rio de Janeiro,Brazil","Center of Study in Telecommunications, PUC-Rio, Rio de Janeiro, Brazil"],"affiliations":[{"raw_affiliation_string":"Center of Study in Telecommunications, PUC-Rio,Rio de Janeiro,Brazil","institution_ids":["https://openalex.org/I2699952"]},{"raw_affiliation_string":"Center of Study in Telecommunications, PUC-Rio, Rio de Janeiro, Brazil","institution_ids":["https://openalex.org/I2699952"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037372511","display_name":"Lisandro Lovisolo","orcid":"https://orcid.org/0000-0002-7404-9371"},"institutions":[{"id":"https://openalex.org/I40034438","display_name":"Universidade do Estado do Rio de Janeiro","ror":"https://ror.org/0198v2949","country_code":"BR","type":"education","lineage":["https://openalex.org/I40034438"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Lisandro Lovisolo","raw_affiliation_strings":["UERJ,Department of Electronics and Telecommunications,Rio de Janeiro,Brazil","Department of Electronics and Telecommunications, UERJ, Rio de Janeiro, Brazil"],"affiliations":[{"raw_affiliation_string":"UERJ,Department of Electronics and Telecommunications,Rio de Janeiro,Brazil","institution_ids":["https://openalex.org/I40034438"]},{"raw_affiliation_string":"Department of Electronics and Telecommunications, UERJ, Rio de Janeiro, Brazil","institution_ids":["https://openalex.org/I40034438"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066814190","display_name":"Luiz da Silva Mello","orcid":"https://orcid.org/0000-0001-7604-8981"},"institutions":[{"id":"https://openalex.org/I2699952","display_name":"Pontifical Catholic University of Rio de Janeiro","ror":"https://ror.org/01dg47b60","country_code":"BR","type":"education","lineage":["https://openalex.org/I2699952"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Luiz Da Silva Mello","raw_affiliation_strings":["Center of Study in Telecommunications, PUC-Rio,Rio de Janeiro,Brazil","Center of Study in Telecommunications, PUC-Rio, Rio de Janeiro, Brazil"],"affiliations":[{"raw_affiliation_string":"Center of Study in Telecommunications, PUC-Rio,Rio de Janeiro,Brazil","institution_ids":["https://openalex.org/I2699952"]},{"raw_affiliation_string":"Center of Study in Telecommunications, PUC-Rio, Rio de Janeiro, Brazil","institution_ids":["https://openalex.org/I2699952"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053063454","display_name":"Carlos Orihuela","orcid":null},"institutions":[{"id":"https://openalex.org/I2699952","display_name":"Pontifical Catholic University of Rio de Janeiro","ror":"https://ror.org/01dg47b60","country_code":"BR","type":"education","lineage":["https://openalex.org/I2699952"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Carlos Orihuela","raw_affiliation_strings":["Center of Study in Telecommunications, PUC-Rio,Rio de Janeiro,Brazil","Center of Study in Telecommunications, PUC-Rio, Rio de Janeiro, Brazil"],"affiliations":[{"raw_affiliation_string":"Center of Study in Telecommunications, PUC-Rio,Rio de Janeiro,Brazil","institution_ids":["https://openalex.org/I2699952"]},{"raw_affiliation_string":"Center of Study in Telecommunications, PUC-Rio, Rio de Janeiro, Brazil","institution_ids":["https://openalex.org/I2699952"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5019652782"],"corresponding_institution_ids":["https://openalex.org/I2699952"],"apc_list":null,"apc_paid":null,"fwci":0.4573,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.61709952,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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/T10262","display_name":"Microwave Engineering and Waveguides","score":0.9962999820709229,"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/T12146","display_name":"Power Line Communications and Noise","score":0.9746999740600586,"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/extremely-high-frequency","display_name":"Extremely high frequency","score":0.8048664927482605},{"id":"https://openalex.org/keywords/path-loss","display_name":"Path loss","score":0.7371967434883118},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.6881373524665833},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6547925472259521},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6170016527175903},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5654271841049194},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5507909655570984},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5037454962730408},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.4997992515563965},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4307311773300171},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.41009536385536194},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40474799275398254},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3365103602409363},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.199912428855896},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1728837490081787},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1594632863998413},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.11458206176757812}],"concepts":[{"id":"https://openalex.org/C45764600","wikidata":"https://www.wikidata.org/wiki/Q570342","display_name":"Extremely high frequency","level":2,"score":0.8048664927482605},{"id":"https://openalex.org/C194273485","wikidata":"https://www.wikidata.org/wiki/Q1478845","display_name":"Path loss","level":3,"score":0.7371967434883118},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.6881373524665833},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6547925472259521},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6170016527175903},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5654271841049194},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5507909655570984},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5037454962730408},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.4997992515563965},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4307311773300171},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.41009536385536194},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40474799275398254},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3365103602409363},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.199912428855896},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1728837490081787},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1594632863998413},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.11458206176757812},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/latincom56090.2022.10000523","is_oa":false,"landing_page_url":"https://doi.org/10.1109/latincom56090.2022.10000523","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land","score":0.5600000023841858}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W429766147","https://openalex.org/W905396345","https://openalex.org/W1895939433","https://openalex.org/W1964357740","https://openalex.org/W1993863804","https://openalex.org/W2002016471","https://openalex.org/W2071189984","https://openalex.org/W2116334496","https://openalex.org/W2145417574","https://openalex.org/W2248726416","https://openalex.org/W2467710037","https://openalex.org/W2487770199","https://openalex.org/W2620784808","https://openalex.org/W2808499605","https://openalex.org/W2889980946","https://openalex.org/W2911964244","https://openalex.org/W2942443632","https://openalex.org/W2944604179","https://openalex.org/W2964124316","https://openalex.org/W2980016373","https://openalex.org/W2986489130","https://openalex.org/W2999507196","https://openalex.org/W3009345526","https://openalex.org/W3014079666","https://openalex.org/W3129785922","https://openalex.org/W3148919794","https://openalex.org/W3181358638","https://openalex.org/W3186604907","https://openalex.org/W4213113494","https://openalex.org/W4298304654","https://openalex.org/W6639520446","https://openalex.org/W6781193508","https://openalex.org/W6992917877","https://openalex.org/W7029244264"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W4386690025","https://openalex.org/W2885778889","https://openalex.org/W4310224730","https://openalex.org/W2766514146","https://openalex.org/W4289703016","https://openalex.org/W2885516856"],"abstract_inverted_index":{"Millimeter-wave":[0],"communication":[1],"systems":[2],"design":[3],"require":[4],"accurate":[5],"path-loss":[6,26,64],"prediction,":[7],"critical":[8],"to":[9,38],"determining":[10],"coverage":[11],"area":[12],"and":[13,51,67],"system":[14],"capacity.":[15],"In":[16],"this":[17],"work,":[18],"four":[19],"machine":[20],"learning":[21],"algorithms":[22],"are":[23,42],"proposed":[24],"for":[25,32],"prediction":[27,86],"in":[28],"an":[29],"indoor":[30],"environment":[31],"5G":[33],"millimeter-wave":[34],"frequencies,":[35],"from":[36],"26.5":[37],"40":[39],"GHz.":[40],"They":[41],"artificial":[43],"neural":[44],"network,":[45],"support":[46],"vector":[47],"regression,":[48],"random":[49],"forest,":[50],"gradient":[52],"tree":[53],"boosting.":[54],"We":[55],"compare":[56],"their":[57],"performances,":[58],"including":[59],"extensions":[60],"of":[61,74,88],"the":[62,72,81,85],"empirical":[63,89],"models":[65],"alpha-beta-gamma":[66],"close-in":[68],"frequency-dependent":[69],"exponent":[70],"incorporating":[71],"number":[73],"crossed":[75],"walls.":[76],"The":[77],"results":[78],"show":[79],"that":[80],"ML":[82],"techniques":[83],"improve":[84],"accuracy":[87],"models.":[90]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
