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, Belém 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.

This paper presents a radio propagation model for the UHF band that is designed for an outdoor scenario in the Amazon region of Brazil and comprises city, water, and forest environments. The model is designed for the Mobile and Home Digital Television (M‐DTV and H‐DTV) services. In the case of M‐DTV, the electric field is calculated at user height, while for H‐DTV it considers a fixed antenna on houses roofs. The field calculation is based on Geometrical Optics (GO) and the Uniform Theory of Diffraction (UTD). The results for M‐DTV show a good agreement with measurement data in an Amazonian city (Belém) for 521 MHz. Different parameters of the proposed model are analyzed: the transition zone city‐water, the level of water, the incidence angle in the forest, and electrical parameters for forest. Finally, the comparison that was made between the electric fields for H‐DTV and M‐DTV shows a difference of up to 19 dB.

This letter provides a model using Dyadic Green's Functions for predicting the electric field in mixed paths for UHF. A new approach is used considering propagation in vertical layers to obtain new equations. The development of the model was accomplished in a mixed City-River-Forest path located in the Amazon Region. Measurement data were collected from a digital TV broadcasting transmitter. A comparative analysis was carried out among the proposed model, ITU-R P.1546-5, measurement data and ITU-R P.1546-5 using Millington's curve-fitting. The measurement data and the model using Dyadic Green's Function are in a good agreement. The results confirm the efficiency and applicability of the proposed model with RMS error no greater than 1.5 dB.

The presence of green areas in urbanized cities is crucial to reduce the negative impacts of urbanization. However, these areas can influence the signal quality of IoT devices that use wireless communication, such as LoRa technology. Vegetation attenuates electromagnetic waves, interfering with the data transmission between IoT devices, resulting in the need for signal propagation modeling, which considers the effect of vegetation on its propagation. In this context, this research was conducted at the Federal University of Pará, using measurements in a wooded environment composed of the Pau-Mulato species, typical of the Amazon. Two machine learning-based propagation models, GRNN and MLPNN, were developed to consider the effect of Amazonian trees on propagation, analyzing different factors, such as the transmitter’s height relative to the trunk, the beginning of foliage, and the middle of the tree canopy, as well as the LoRa spreading factor (SF) 12, and the co-polarization of the transmitter and receiver antennas. The proposed models demonstrated higher accuracy, achieving values of root mean square error (RMSE) of $3.86$ dB and standard deviation (SD) of $3.8614$ dB, respectively, compared to existing empirical models like CI, FI, Early ITU-R, COST235, Weissberger, and FITU-R. The significance of this study lies in its potential to boost wireless communications in wooded environments. Furthermore, this research contributes to enhancing more efficient and robust LoRa networks for applications in agriculture, environmental monitoring, and smart urban infrastructure.

This paper describes a radio propagation model for a mixed environment formed by rainforest and river in Brazil's Amazon region. The model uses Geometrical Optics and the Uniform Theory of Diffraction for the calculation of the electric field at the receiver, accounting for the different transmitted, reflected and diffracted components in each of the environments. Simulation results are compared with measurements performed in Belém (Para) at 521 MHz, showing a good fit, with an average Root Mean Square Error (RMSE) over all measured paths of 3.3 dB and a maximum one of 4.2 dB. The proposed model performs much better than the ITU-R P. 1546 and the Okumura Hata ones, where the comparison with measurements leads to an RMSE higher than 8 dB. In addition, by using Genetic Algorithms, the electrical parameters for the dense forest are obtained, enabling to slightly decrease (0.14 dB on the average of all measured paths) the RMSE of the proposed model.

This letter presents an analysis of the radio propagation channel based on measurements at the 3.5 GHz frequency. The measurement campaigns were carried out inside the Valde-Cans airport using line-of-sight (LOS) transmissions. First, the channel small-scale dispersion parameters were extracted through channel probing and the results are similar to those obtained by ITU-R P.1238 for the bands below 15 GHz considering commercial indoor environments as well as for those using the 3.5 GHz band in outdoor environments utilizing WiMax OFDM-256 signals. Then, the floating-intercept (FI) and close-in (CI) models are applied and analyzed to evaluate the received signal behavior for co-polarized and cross polarized antennas. The results show that the CI path loss exponent values are close to the free space propagation loss model, while the FI model provides a lower root mean square error (RMSE) to the measured data. The results show that the FI and CI models are suitable for large-scale indoor propagation loss modeling for 5G networks with a frequency of 3.5 GHz.