A(s) Independência(s) do Brasil e suas comemorações: reflexões sobre passado e presente

Sousa, Maria Aparecida Silva de; Oliveira, Nora de Cássia Gomes de

doi:10.22481/politeia.v21i2.13039

Cited by 1 publication

(2 citation statements)

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“…To distinguish the true multipath from the impulsive noise, it is necessary to perform filtering on the power delay profiles. A technique has been constantly used for this purpose is the CFAR filtering technique [41]. The good results provided by the CFAR technique [45][46][47][48][49][50] accredit it as a valid technique and widely accepted by the academic community.…”

Section: B Application Of Artificial Neural Network As a Filtering Te...mentioning

confidence: 99%

“…The application of the CFAR technique oriented to delay profile filtering is initially based on the determination of a noise threshold for each of the PDPs obtained in the field measurements. This noise threshold is determined according to what is presented in [41]. Then all echoes from each PDP will be analyzed.…”

Section: B Application Of Artificial Neural Network As a Filtering Te...mentioning

confidence: 99%

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Artificial Intelligence Enabled Radio Propagation: Path Loss Improvement and Channel Characterization in Vegetated Environments

Gonsioroski,

Santos,

Viana

et al. 2024

J. Microw. Optoelectron. Electromagn. Appl.

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In this paper, the application of AI and machine learning (ML) to the study of wireless propagation channels is investigated in two parts: first, an artificial neural network model is used to improve path loss prediction, and then, a pattern recognition model using multilayer perceptron (MLP) networks is used to identify and remove impulsive noise in power delay profiles (PDP). These studies were conducted based on field measurements in the 2400 MHz band in a public square with vegetation. The results are analyzed and compared with ordinary least squares (OLS) nonlinear regression results and results from similar studies. The Root Mean Square Error (RMSE) values between the experimental results of mean path loss and those provided by each propagation model are presented. The adjustment performed by OLS nonlinear regression and ANN significantly reduced the RMSE. The best results are those presented by artificial neural networks, with RMSE of 0.39 when using four neurons in the hidden layer of the ANN. The ANN used to identify and remove impulsive noise in power delay profiles (PDP) through pattern recognition proved to be more efficient than the CFAR technique. ANN technique found a larger number of valid multipaths compared to the CFAR technique.

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Section: B Application Of Artificial Neural Network As a Filtering Te...mentioning

confidence: 99%

Section: B Application Of Artificial Neural Network As a Filtering Te...mentioning

confidence: 99%

Artificial Intelligence Enabled Radio Propagation: Path Loss Improvement and Channel Characterization in Vegetated Environments

Gonsioroski,

Santos,

Viana

et al. 2024

J. Microw. Optoelectron. Electromagn. Appl.

View full text Add to dashboard Cite

show abstract

A(s) Independência(s) do Brasil e suas comemorações: reflexões sobre passado e presente

Cited by 1 publication

References 10 publications

Artificial Intelligence Enabled Radio Propagation: Path Loss Improvement and Channel Characterization in Vegetated Environments

Artificial Intelligence Enabled Radio Propagation: Path Loss Improvement and Channel Characterization in Vegetated Environments

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