Navegando por Assunto "Propagation models"

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Acesso aberto (Open Access)
Aplicação de redes neurais artificiais para predição de RSSI e SNR em ambiente de bosque amazônico
(Universidade Federal do Pará, 2024-06-11) BARBOSA, Brenda Silvana de Souza; ARAÚJO, Jasmine Priscyla Leite de; http://lattes.cnpq.br/4001747699670004; https://orcid.org/0000-0003-3514-0401; BARROS, Fabrício José Brito; http://lattes.cnpq.br/9758585938727609
The presence of green areas in urbanized cities is crucial to reduce the negative impacts of urbanization. However, these areas can influence the signal quality of IoT devices that use wireless communication, such as LoRa technology. Vegetation attenuates electromagnetic waves, interfering with data transmission between IoT devices, resulting in the need for signal propagation modeling that considers the effect of vegetation on its propagation. In this context, this research was conducted at the Federal University of Pará, using measurements in a wooded environment composed of the Pau-Mulato species, typical of the Amazon. Two propagation models based on machine learning, GRNN and MLPNN, were developed to consider the effect of Amazonian trees on propagation, analyzing different factors such as the height of the transmitter relative to the trunk, the beginning of the foliage, and the middle of the tree canopy, as well as the LoRa spreading factor (SF) 12 and the copolarization of the transmitter and receiver antennas. The best models were the machine learning ones, GRNN and MLPNN, which demonstrated greater accuracy, achieving root mean square error (RMSE) values of 3.86 dB and 3.8614 dB, and standard deviation (SD) of 3.8558 dB and 3.8564 dB, respectively. On the other hand, compared to classical models in the literature, the best-performing model was the Floating Intercept (FI) model, with RMSE and SD errors around 7.74 dB and 7.77 dB, respectively, while the FITU-R model had the highest RMSE and SD errors, around 26.40 dB and 9.65 dB, respectively, for all heights and polarizations. Furthermore, the importance of this study lies in its potential to boost wireless communications in wooded environments, as it was observed that even at short distances at heights of 12 m and 18 m, the SNR (Signal-to-Noise Ratio) had lower values due to the influence of the foliage, but it was still possible to send and receive data. Finally, it was shown that vertical polarization achieved the best results for the Amazon forest environment.
Acesso aberto (Open Access)
Modelagem de par-trançado para comunicações em banda larga
(Universidade Federal do Pará, 2016-03-07) BORGES, Gilvan Soares; COSTA, João Crisóstomo Weyl Albuquerque; http://lattes.cnpq.br/9622051867672434
Ultimately, the purpose of a model for transmission lines is to describe how the transmission properties of a line are frequency dependent. The sources of such dependency can be grouped in two areas: that one related to longitudinal changes on the line geometry and constituting materials (non-uniformities), and that related to electromagnetic phenomena such as the skin effect and the dielectric dispersion (present even when the line is uniform). The contribution of this thesis is related to the above mentioned areas, focused on a specific type of transmission line, the twisted-pair. The models for twisted pairs found in literature assume simplistic considerations which are not always realistic, e.g., they ignore that the dielectric medium is heterogeneous and with losses, ignore the effect of the non-uniformities that are inevitable to all transmission lines, etc. These and other issues are taken into account during the development of a new twisted-pair model. This model is composed for two components, a deterministic one which is a function of the constructive characteristics of the twisted-pair, and a stochastic one which is a function of the inherent defects in twisted-pair cables. Regarding the deterministic component, it employs a realistic and straightforward approach to describe: the proximity effect of the conductors, the presence of conductors’ insulation, the dielectric losses and pair twisting. As a result, the model is more accurate than the models from the literature. Regarding the stochastic component, it was not found in literature similar models for comparison. Nevertheless, it was shown that the proposed stochastic model has good agreement with experimental observation.