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.

Dynamic Regressor/Ensemble Selection for a Multi-Frequency and Multi-Environment Path Loss Prediction

“…However, the RMSE value of 4.2061 dB by our proposed method is acceptable as it is less than 7 dB. It should be noted that results in [63,64,67] were obtained when the search space of 1:190 was used for the maximum features' hyper-parameter in RF, ET, and GB. Meanwhile, good results were obtained from other datasets with just a search space of 1:10.…”

“…RMSE values for each route lies within the range 3.80 dB-6.54 dB. Improvements were made to results in [63,[65][66][67] by the proposed method and a reduction of accuracy was observed for [64] because the RMSE value was greater than that of the original work, as presented in Table 7. However, the RMSE value of 4.2061 dB by our proposed method is acceptable as it is less than 7 dB.…”

“…The proposed method was tested on datasets used in [63][64][65][66][67], and comparisons were made with results in the original works in terms of RMSE, as presented in Table 7. Although RMSE values were not presented in [65], the path loss exponent, mean, and standard deviation of the shadowing component for each of the two environments were presented.…”

“…In [68], a dataset analysis was presented and the dataset was used as a subset in [66] to develop a model that resulted in an RMSE value of 6.27 dB. For the work by Carvalho et al [67], results of 4 routes were presented, 3 of which are for suburban environments, and the fourth one is an urban environment. RMSE values for each route lies within the range 3.80 dB-6.54 dB.…”

Hybrid computational and real data-based positioning of small cells in 5G networks

“…However, in this study, an ECC-33 model was chosen, which is adapted to the urban but forested environments of the city of Belém, as proposed by de Carvalho et al (2021) .…”