Abstract
The establishment and improvement of transmission systems rely on models that take into account, (among other factors), the geographical features of the region, as these can lead to signal degradation. This is particularly important in Brazil, where there is a great diversity of scenery and climates. This article proposes an outdoor empirical radio propagation model for Ultra High Frequency (UHF) band, that estimates received power values that can be applied to non-homogeneous paths and different climates, this last being of an innovative character for the UHF band. Different artificial intelligence techniques were chosen on a theoretical and computational basis and made it possible to introduce, organize and describe quantitative and qualitative data quickly and efficiently, and thus determine the received power in a wide range of settings and climates. The proposed model was applied to a city in the Amazon region with heterogeneous paths, wooded urban areas and fractions of freshwater among other factors. Measurement campaigns were conducted to obtain data signals from two digital TV stations in the metropolitan area of the city of Belém, in the State of Pará, to design, compare and validate the model. The results are consistent since the model shows a clear difference between the two seasons of the studied year and small RMS errors in all the cases studied.
Highlights
Digital television systems are designed to ensure the received signal for the users is of a high standard
In [19] the authors employ a new methodology to design the guided propagation of radio waves. This methodology is called the Moving Window Finite Difference Time Domain (MWFDTD), which makes an improvement in classical FDTD because it assumes that a pulsed radio wave only exists in a small part of the propagation path at a given period of time
knowledge-based theory (KBT) carries out the training with one dataset, but the other sets are unknown to the model
Summary
Digital television systems are designed to ensure the received signal for the users is of a high standard. Empirical and deterministic propagation models that allow corrections/modifications for different types of terrain morphology have been discussed in recent years. This paper puts forward an empirical radio propagation model for DTV for non-homogeneous paths and different climates based on machine learning techniques. The proposed model has two innovative features: i) its application in non-homogeneous paths includes long stretches of fresh water; ii) it distinguishes between the seasons These two factors have been poorly studied in the UHF range, as long stretches of fresh water are common in towns and cities located in equatorial/tropical forests. Data from a measurement campaign carried out in Belem city located in the Amazon estuary was used for purposes of comparison and to validate the model
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