Modeling and inferring the attenuation induced by vegetation barriers at 2G/3G/4G cellular bands using Artificial Neural Networks

Gómez-Pérez, Paula; Crego-Garcia, Marcos; Cuiñas, Íñigo; Caldeirinha, Rafael F. S.

doi:10.1016/j.measurement.2016.12.014

Cited by 13 publications

(8 citation statements)

References 11 publications

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“…Gómez-Pérez et al [27] recommended the use of ANN for attenuation modeling prompted by vegetation barriers at cellular frequency bands of 2G to 4G. MLP architecture was used, and trained with measurements of various configurations of vegetation barriers (such as barrier thickness, vegetation density, foliage, polarization, frequency, trunk density of vegetation specie and receiver position along the linear rail) as well as vegetation species at frequency bands of 900, 1800 and 2100MHz.…”

Section: Review Of Related Work and Main Contributionsmentioning

confidence: 99%

“…Meanwhile, VHF signal range is 30-300 MHz. However, majority of the reviewed work focused on UHF networks [9], [15]- [17], [19], [14], [22], [23], [13], [25]- [34]; two studies focused on Super High Frequency (SHF) network [21], [24]; while the remaining seven studies employed simulated data for ANN-based path loss modelling [18]- [20], [23], [25], [27], [32]. Given the same propagation environment, the behaviour of radio signals often changes with varying transmission frequency.…”

Section: Review Of Related Work and Main Contributionsmentioning

confidence: 99%

“…In order to ensure good ANN model generalization, the developed model must perform well when tested with data that are different from the previously used training data. In the literature [9], [14]- [27], [13], [30]- [34], developed ANN path loss models were tested with data obtained from radio networks that operate at the same signal frequency as that of the training data. In this new study, ANN path loss model was developed using RSS data obtained from VHF network (NTA-BENIN, 189.25 MHz); while the generalization ability of the developed ANN path loss model was tested using six subsets of data collected from UHF network (ITV-BENIN, 479.25 MHz).…”

Section: Review Of Related Work and Main Contributionsmentioning

confidence: 99%

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Determination of Neural Network Parameters for Path Loss Prediction in Very High Frequency Wireless Channel

et al. 2019

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It is very important to understand the input features and the neural network parameters required for optimal path loss prediction in wireless communication channels. In this paper, an extensive investigation was conducted to determine the most appropriate neural network parameters for path loss prediction in Very High Frequency (VHF) band. Field measurements were conducted in an urban propagation environment to obtain relevant geographical and network information about the receiving mobile equipment and quantify the path losses of radio signals transmitted at 189.25 MHz and 479.25 MHz. Different neural network architectures were trained with varying kinds of input parameters, number of hidden neurons, activation functions, and learning algorithms to accurately predict corresponding path loss values. At the end of the experimentations, the performance of the developed Artificial Neural Network (ANN) models are evaluated using the following statistical metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Standard Deviation (SD) and Regression coefficient (R). Results obtained show that the ANN model that yielded the best performance employed four input variables (latitude, longitude, elevation, and distance), nine hidden neurons, hyperbolic tangent sigmoid (tansig) activation function, and the Levenberg-Marquardt (LM) learning algorithm with MAE, MSE, RMSE, SD and R values of 0.58 dB, 0.66 dB, 0.81 dB, 0.56 dB and 0.99 respectively. Finally, a comparative analysis of the developed model with Hata, COST 231, ECC-33 and Egli models showed that ANN-based path loss model has better prediction accuracy and generalization ability than the empirical models.INDEX TERMS Artificial neural network, path loss, radio propagation, wireless channel, machine learning.

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Section: Review Of Related Work and Main Contributionsmentioning

confidence: 99%

Section: Review Of Related Work and Main Contributionsmentioning

confidence: 99%

Section: Review Of Related Work and Main Contributionsmentioning

confidence: 99%

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Determination of Neural Network Parameters for Path Loss Prediction in Very High Frequency Wireless Channel

et al. 2019

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“…Besides, there are studies in the literature in which the effect of indoor obstacles on electromagnetic pollution is determined by artificial neural networks (Gomez-Perez et al, 2017). However, since these studies are carried out in an indoor environment and limited area, the number of sources causing electromagnetic pollution is quite limited.…”

Section: Resultsmentioning

confidence: 99%

A comparative analysis of the development of electromagnetic pollution maps with artificial intelligence and interpolation

Ugur¹

2023

Sci. Res. Essays

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Cell phones and mobile devices have been an integral part of life over the past 30 years. In parallel with the intensity of use of mobile phones, the number of base stations has also increased. The increasing number of base stations has caused an intensity of electromagnetic pollution in the GSM band. The issue of electromagnetic pollution has become increasingly remarkable due to the claims that it can cause diseases and scientific studies carried out in this field. The number and variety of studies carried out in the last 30 years in this regard has also increased. Base stations are established with various features. While some kinds of base stations use radial antenna type, some of them use directional antennas. Depending on the region where it is established, the cell types it uses may also differ. Therefore, multiple measurements are required to analyse the impact of a base station. However, it is practically not possible to make an infinite number of measurements. Within the scope of this study, it is aimed to refine and clarify the electromagnetic pollution maps by using artificial intelligence methods. As a result of the studies carried out in this area, electromagnetic pollution maps have become much more detailed.

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“…According to their results, the prediction performance of the wavelet neural network exceeded that of the RBF-NN. Other types of activation functions such as the hyperbolic tangent (tanh) [ 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ] and sigmoid functions [ 71 , 72 , 73 , 74 , 75 ] have also been used in ANNs for path loss prediction.…”

Section: Related Workmentioning

confidence: 99%

Accurate Path Loss Prediction Using a Neural Network Ensemble Method

Kwon,

Son

2024

Sensors

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Path loss is one of the most important factors affecting base-station positioning in cellular networks. Traditionally, to determine the optimal installation position of a base station, path-loss measurements are conducted through numerous field tests. Disadvantageously, these measurements are time-consuming. To address this problem, in this study, we propose a machine learning (ML)-based method for path loss prediction. Specifically, a neural network ensemble learning technique was applied to enhance the accuracy and performance of path loss prediction. To achieve this, an ensemble of neural networks was constructed by selecting the top-ranked networks based on the results of hyperparameter optimization. The performance of the proposed method was compared with that of various ML-based methods on a public dataset. The simulation results showed that the proposed method had clearly outperformed state-of-the-art methods and that it could accurately predict path loss.

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Modeling and inferring the attenuation induced by vegetation barriers at 2G/3G/4G cellular bands using Artificial Neural Networks

Cited by 13 publications

References 11 publications

Determination of Neural Network Parameters for Path Loss Prediction in Very High Frequency Wireless Channel

Determination of Neural Network Parameters for Path Loss Prediction in Very High Frequency Wireless Channel

A comparative analysis of the development of electromagnetic pollution maps with artificial intelligence and interpolation

Accurate Path Loss Prediction Using a Neural Network Ensemble Method

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