A UHF Path Loss Model Using Learning Machine for Heterogeneous Networks

In this paper, we present and evaluate a new propagation model for heterogeneous networks. The designed model is multiband, multienvironment, and is usable for short and long distance. For this research, a measurement campaign was conducted in Tunis (Tunisia) using continuous wave analog technology. It concerns the most used bands (450, 850, 1800, 2100, and 2600 MHz) in rural, suburban, and urban environments. Measurements are split into two independent and random sets. The first one is used for model training, whereas the second is used for model validation. The new model is based on neural networks, uses back propagation algorithm, and obtains its inputs from Standard Propagation Model, to which we have added more parameters such as frequency, environment type, land use distribution, and diffraction loss. Model variables are computed from accurate Digital Terrain Model and Land Used maps with 2-m resolution. The statistical analysis has shown that the developed model is accurate as we obtained the following metrics: 0.235-dB absolute mean error, 6.850-dB standard deviation, and 85% correlation factor. The obtained simulation results are then compared to SPM and ITU-R P.1812-4 prediction, which are taken as reference to highlight the benefit of the new model.