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Novel Lee Model for Prediction of Propagation Path Loss in Digital Terrestrial Television Systems in Montevideo City, Uruguay

  • Juan M. Torres-Tovio
  • Nelson A. Pérez-GarcíaEmail author
  • Angel D. Pinto-Mangones
  • Mario R. Macea-Anaya
  • Samir O. Castaño-Rivera
  • Enrique I. Delgado Cuadro
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1066)

Abstract

During the planning and dimensioning (P&D) of a Digital Terrestrial Television (DTT) system plays a very important role to use a suitable model that allows estimating the propagation path loss with the most possible precision according to the typical propagation characteristics of the site in which the system will be implemented. The imprecision in that estimation will lead to oversize or undersize of the system. In this sense, in this paper, one of the propagation most used models for the PyD process in ultra high frequency (UHF) band, in which the DTT systems operate, as the Lee model, is optimized using measurements carried out in Montevideo city, Uruguay, and also using Ant Colony Optimization (ACO) computational intelligence technique. The performance presented by new Lee model, compared with the performance shown by propagation models such as Okumura-Hata, Hata-Davidson, TDT-Uruguay and original Lee model, was the best, with a root mean square error (RMSE) of 9.43 dB.

Keywords

Digital Terrestrial Television Planning and dimensioning Lee model propagation Ant Colony Optimization 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Juan M. Torres-Tovio
    • 1
  • Nelson A. Pérez-García
    • 2
    Email author
  • Angel D. Pinto-Mangones
    • 1
  • Mario R. Macea-Anaya
    • 3
  • Samir O. Castaño-Rivera
    • 3
  • Enrique I. Delgado Cuadro
    • 4
  1. 1.Universidad del SinúMontería, CórdobaColombia
  2. 2.Universidad de Los AndesMéridaVenezuela
  3. 3.Universidad de CórdobaMontería, CórdobaColombia
  4. 4.Universidad Técnica de BabahoyoBabahoyoEcuador

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