Empirical radio propagation model for DTV applied to non-homogeneous paths and different climates using machine learning techniques

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.

Application of Hybrid ARIMA and Artificial Neural Network Modelling for Electromagnetic Propagation: An Alternative to the Least Squares Method and ITU Recommendation P.1546-5 for Amazon Urbanized Cities

“…Another way of improving this model is to isolate the weather influence. It would be done by measuring each point again on the rainy season, as in [2], and analysing the series acquired on this second measurement campaign. A third suggestion on how to improve this study is testing another hybrid technique in the same problem and assessing if there is one more suited to the electromagnetic propagation modelling.…”

“…After analyzing the ACF and PACF graphs for the series under study, we decided to employ an ARIMA (2, 0, 0) model to fit the training data. is is represented by equation (2).…”

“…We represent variables that are originated from interpolated datasets with the symbol "∼" above the variable letter, as seen when comparing equation (3) with equation (2). When proceeding in an analogous way to the original datasets group, the fitting process for the interpolated series gave, as its best result, an ARIMA (4, 0, 0) model expressed as in equation (4).…”

“…Nonetheless, it should be taken into account that there is a scarcity of wave propagation models in the literature adapted for towns and cities in the Amazon region or those near the Equator line. Weather itself is a key factor in the effectiveness of telecommunication services in this kind of region, as shown in [2].…”

A novel 3D ray launching technique for radio propagation prediction in indoor environments

“…The application of smart radio propagation modelling to assist smart city design, 4G mobile networks further upgradation, Internet of Things, and the evaluation of higher frequency based 5G network system [2]. Radio propagation is a smart way of analysing and predicting the signal details [3]. Even though direct measurements enable accurate evaluation of onsite performance, but it requires a considerable amount of time and efforts.…”