Amazon Large-Scale Channel Modeling at 3.5GHz Based on Bio-Inspired Optimization

This paper focuses on a large-scale wireless channel modeling for 3.5 GHz frequency, consisting of a fine-tuning of the Close-In (CI) and Floating-intercept (FI) propagation models. This is achieved through the use of bio-inspired optimization techniques, genetic algorithms (GA) and Particle Swarm Optimization (PSO). The study is contextualized in outdoor scenario, with particular emphasis on the Rio Negro Bridge, in Manaus, state of Amazonas, Brazil, with a measurement campaign focused on signal strength data. The goal is to characterize the average behavior of path loss in this specific environment. The results of this research include the identification of the most appropriate model coefficients (CI and FI) that fit the measured data, with the fitted CI and FI models having the smallest values of Root Mean Square Error (RMSE) of 4.28 dB and 4.14 dB respectively compared to classic path loss models from the literature, such as Okumura-Hata (7.55 dB), ECC-33 (8.54 dB) and Ericsson (16.9 dB). Consequently, the model adapted in this study has the potential to make a significant contribution to the strategic planning and implementation of 5G network expansion in the Amazon region.