Adjusting Large-Scale Propagation Models for the Amazon Region Using Bioinspired Algorithms at 1.8 and 2.6 GHz Frequencies

Abstract

In this work, a new method employs Bioinspired Computational (BIC) optimization from the genetic algorithm, bat algorithm, and flower pollination algorithm. Robust and accurate modeling of the input parameters adjusts the propagation models Stanford University Interim, Electronic Communication Committee, and Floating Interception that consider environments with characteristics specifically of urban regions in the Amazon. The lack of research related to the development of propagation models for Amazonian environments motivated this work. Thus, this application proves the effectiveness of using BIC techniques for modeling the communication channel. Measurement campaigns were carried out in the city of Belem, Brazil, for large-scale channel modeling on the frequencies of 1.8 and 2.6 GHz, belonging to the long-term evolution or fourth-generation mobile communications system (4G). After being adjusted by the optimum values calculated by the BIC techniques used, the models showed better results compared to modeling without optimization. Additionally, it was verified an error reduction of about 80% concerning the metrics root-mean-square error and standard deviation.

Index Terms
Channel Characterization; Measurement campaigns; Bioinspired Computational; Propagation Models