Application of Artificial Neural Networks for Prediction of Received Signal Strength Indication and Signal-to-Noise Ratio in Amazonian Wooded Environments  
[PERSON]. [PERSON]  
1 Electrical Engineering Graduate Department, Universidade Federal do Para, [PERSON], 01, Guam, Belem 66075-110, Brazil; [EMAIL_ADDRESS] (H.A.O.C. ); [EMAIL_ADDRESS] (A.S.M. ); [EMAIL_ADDRESS] (C.M.M.C); [EMAIL_ADDRESS] (F.C.F. ); [EMAIL_ADDRESS] (F.J.B.B. ); [EMAIL_ADDRESS] (G.P.S.C. ); [EMAIL_ADDRESS] (F.J.B.B. );  
Footnote 1: email: {Brenda,b,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c  
These factors cause high variability in the quality of the received signal, affecting the reliability and performance of wireless communication systems. Therefore, it is necessary to carry out studies to guarantee the reliability and coverage of the connection, making it essential to develop propagation loss models for the most diverse environments [4]. Several studies have addressed the modeling of communication channels, considering different wireless communication technologies. Among these technologies, Long-Range (LoRa) has stood out, as it provides long-range communication and low energy consumption, as pointed out by [5; 6]. Moreover, a single receiver in the LoRa network can manage multiple nodes distributed in the environment, leading to significant improvements in IoT applications and contributing to reduce the costs associated with IoT systems deployment. In addition, channel modeling also varies according to the characteristics of the surrounding area. The combination of dense vegetation, topographical variations, high humidity, remote locations, and wildlife interference makes the Amazon rainforest a particularly challenging environment for wireless signal propagation. This environmental diversity represents a challenge for planning systems to design and deploy IoT applications efficiently. Without understanding the appropriate channel model, it is difficult to analyze coverage and predict optimal deployment configurations. Empirical propagation loss models in different outdoor environments have been proposed in [7; 8; 9], acknowledging that the type of terrain significantly interferes with signal propagation and the importance of accurate models to assist in decision-making for the wireless sensor networks' (WSN) deployment. Therefore, outdoor environments with heterogeneous vegetation have the special characteristic of acting as spreaders of electromagnetic waves. The excessive spread attenuates the signal, limiting the performance expected for 5G mobile wireless communications [10]. Works such as [11; 12; 13] mentioning the use of machine learning (ML) techniques to predict propagation loss has been progressively growing. The use of ML techniques to model the wireless signal channel improves the propagation loss prediction in different environments. However, to use ML models, it is necessary to realize data acquisition, which will be employed to train different models. In addition, to the best of the authors' acknowledge, few are the works that collect data to represent signal propagation in an Amazon wooded environment, as seen in [14; 15; 16; 17; 18; 19]. In this context, this research aims to develop an ML model that represents the channel behavior in an Amazon wooded environment. For this purpose, a measurement campaign was conducted in the Camilo Vianna forest, located within the Federal University of Para (UFPA). Furthermore, the developed ML model was compared with classical models present in the literature to verify the model quality.