{"title":"Neural predictive models for Brazilian digital TV system based on the azimuthal orientation","authors":"A. Pereira, I. Casella","doi":"10.1109/ISCE.2016.7797365","DOIUrl":null,"url":null,"abstract":"Due to the shutdown of the analog TV signal transmissions and the proliferation of new installations of the Brazilian digital TV system throughout the country, it is necessary a deeper understanding of the main characteristics of the digital TV propagation channels and how to precisely estimate the received power in the new TV devices to an efficient deployment project. In general, the existing propagation models are not very accurate due to specific terrain variations and intrinsic stochastic propagation characteristics of each region. In this way, this work proposes two new neural predictive models based on the azimuthal orientation. The proposed models are analyzed and compared to the most important models disclosed in the literature.","PeriodicalId":193736,"journal":{"name":"2016 IEEE International Symposium on Consumer Electronics (ISCE)","volume":"267 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Symposium on Consumer Electronics (ISCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCE.2016.7797365","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Due to the shutdown of the analog TV signal transmissions and the proliferation of new installations of the Brazilian digital TV system throughout the country, it is necessary a deeper understanding of the main characteristics of the digital TV propagation channels and how to precisely estimate the received power in the new TV devices to an efficient deployment project. In general, the existing propagation models are not very accurate due to specific terrain variations and intrinsic stochastic propagation characteristics of each region. In this way, this work proposes two new neural predictive models based on the azimuthal orientation. The proposed models are analyzed and compared to the most important models disclosed in the literature.
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