Browsing by Autor "Leonardo Henrique Gonsioroski"

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Artificial Intelligence Enabled Radio Propagation: Path Loss Improvement and Channel Characterization in Vegetated Environments
(Sociedade Brasileira de Microondas e Optoeletrônica; Sociedade Brasileira de Eletromagnetismo, 2024) Leonardo Henrique Gonsioroski; Amanda Santos; Jairon Viana; Sandra Ferreira; Rogerio Silva; Luiz da Silva Mello; Leni Matos; Marcelo Molina Silva
In this paper, the application of AI and machine learning (ML) to the study of wireless propagation channels is investigated in two parts: first, an artificial neural network model is used to improve path loss prediction, and then, a pattern recognition model using multilayer perceptron (MLP) networks is used to identify and remove impulsive noise in power delay profiles (PDP). These studies were conducted based on field measurements in the 2400 MHz band in a public square with vegetation. The results are analyzed and compared with ordinary least squares (OLS) nonlinear regression results and results from similar studies. The Root Mean Square Error (RMSE) values between the experimental results of mean path loss and those provided by each propagation model are presented. The adjustment performed by OLS nonlinear regression and ANN significantly reduced the RMSE. The best results are those presented by artificial neural networks, with RMSE of 0.39 when using four neurons in the hidden layer of the ANN. The ANN used to identify and remove impulsive noise in power delay profiles (PDP) through pattern recognition proved to be more efficient than the CFAR technique. ANN technique found a larger number of valid multipaths compared to the CFAR technique.
Evaluation of Cooperative Cognitive Radio System for White Spectral Space Detection using the Covariance Detector
(2025) Rafael G. Ramirez Montecinos; Mateo I. Luna Rico; Marcelo Molina Silva; Jussif J. Abularach Arnez; Luiz da Silva Mello; Carlos V. Rodriguez R.; Leonardo Henrique Gonsioroski
Wireless spectrum is increasingly scarce, which motivates the need for robust methods to detect unused bands—especially under challenging conditions like low SNR and fading. This study proposes integrating Spectral Covariance Sensing (SCS) into a cooperative cognitive radio framework, leveraging hard-decision fusion schemes (AND, OR, Majority) to enhance detection stability. Using real Advanced Television Systems Committee(ATSC) signal data, the detection performance was evaluated across various SNR levels. The results show that cooperative sensing significantly improves detection probability under low SNR, with the OR rule achieving the highest detection rate (e.g., ≈90% at –30 dB) and the majority rule providing the best overall trade-off between reliability and false alarms. These findings demonstrate the practical value of cooperative SCS systems in dynamic spectrum environments.
Preliminary Results of the Indoor Coverage Field Tests of the Advanced ISDB-T System in Brazil
(2023) Amanda B. Santos; Leonardo Henrique Gonsioroski; Rodrigo Ribeiro de Oliveira; Luiz da Silva Mello; Alberto Leonardo Penteado Botelho; Cristiano Akamine; Natália C. Fernandes; Marcelo Molina Silva
This article presents the preliminary results of field tests with the Advanced Integrated Services Digital Broadcasting for Terrestrial Television Broadcasting (ISDB- T) system in indoor environments. Received channel power and error-free system reception threshold records were performed at four different measurement sites. Five different H/V dual-polarized MIMO antennas are used in measurements to evaluate system performance when using antennas from different manufacturers. The results show that the advanced ISDB-T system is robust to indoor penetration losses and when subjected to the use of different antennas.