Abstract
This work presents and evaluates the use of geometric parameters of the environment in the prediction of the electric field in mixed city-river type environments, employing two techniques of Machine Learning (ML) as Artificial Neural Networks (ANN) and Neuro-Fuzzy System (NFS). For its development, measurements were carried out in Amazon Region, Belem city, in the 521 MHz band. The input parameters for an ANN and NFS are the distance between transmitter and receiver, the distance only over the river, the height of the ground, the radius of the first Fresnel ellipsoid, and the electric field of free space. The ANN is a Multilayer Perceptron Network (MLP) that uses the Levenberg-Marquardt training algorithm and cross-validation method. The NFS is an Adaptive Neuro-Fuzzy Inference System (ANFIS) that uses the model Sugeno. The results obtained compared with the classic literature models (ITU-R 1546 and Okumura-Hata) in the city for distances up 20 km and over the river for distances up 5 km. A quantitative analysis is performed between the measured and predicted data through the Standard deviation (SD), Root Mean Square Error (RMSE), and the Grey Relational Grade, combined with the Mean Absolute Percentage Error (GRG-MAPE). For ANN, the SD is 2.13, the RMSE is 2.11 dB, and the GRG-MAPE is 0.96. Also, for the NFS, the SD is 1.99, the RMSE is 2.06 dB, and the GRG-MAPE is 0.97. It should be noted that the transition zone between the city and the river was characterized by the proposed ANN and NFS in contrast with the classic literature models, which did not demonstrate coherence in the transition zone.
Highlights
Several countries around the World use rivers for commerce, transportation, and tourism
In the propagation of radio waves, several factors can influence the electric field level in the path from the transmitter to the receiver, such as the transition through different types of environments, such as the height of buildings, as well as the types of vegetation and if the waves travel over areas without vegetation or over areas covered with water [2]
A recent work presents a radio propagation model called Inland for a mixed land-river path [8], and it based in the Round Earth Loss model (REL) with a modification of the free space equation, which replaced with the equations of Okumura-Hata
Summary
Several countries around the World use rivers for commerce, transportation, and tourism. A recent work presents a radio propagation model called Inland for a mixed land-river path [8], and it based in the Round Earth Loss model (REL) with a modification of the free space equation, which replaced with the equations of Okumura-Hata. The present work proposes two radio propagation models using ANN type Multiple Layer Perceptron (MLP) and NFS type Adaptive Neuro-Fuzzy Inference System (ANFIS), both for a mixed city-river path. These proposed models have as one of its objectives to implement inputs quickly to be calculated, in contrast with [11], which use as one of its inputs the uniform theory of diffraction that involves complex calculations. Considering the characteristic of these models and their application will be called a Geometric Model for Mixed Paths using Neural Networks (GMMP-NN) and Neuro-Fuzzy (GMMP-NF)
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