Comparative experimental study on modeling the path loss of an UWB channel in a mine environment using MLP and RBF neural networks

Zaarour, Nour; Kandil, Nahi; Hakem, Nadir; Despins, Charles

doi:10.1109/icwcuca.2012.6402503

Cited by 12 publications

(9 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zaarour et al [24] present an experimental study for modelling path loss in UWB channel in a mine environment by implementing Radial Basis Function (RBF) and MLP with a focus on variations in path loss attenuation with respect to distance and frequency. This was considered a different approach in path loss modeling in a mine environment.…”

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%

See 1 more Smart Citation

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

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Section: Review Of Related Work and Main Contributionsmentioning

confidence: 99%

Section: Review Of Related Work and Main Contributionsmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Popescu et al [35] To study the application of neural networks to the prediction of propagation path loss in urban and suburban environments Feed forward neural networks Sotiroudis et al [36] To propose an alternative neural network algorithm for the prediction of propagation path loss in urban environments ANN Oustlin et al [34] To analyze ANN models used for macrocell path loss estimation ANN Kalakh et al [37] To present an ultrawide band propagation channel modeling with neural networks in a mine environment ANN Zaarour et al [38] To use MLP and RBF artificial neural networks to study ultrawide band communication channels ANN: multilayer perception (MLP) and radial basis function (RBF) Sotiroudis et al [39] To produce an alternative procedure for predicting propagation path loss in urban environments…”

Section: Authors Aim Methodsmentioning

confidence: 99%

“…e developed model was compared with the experimental measured values and was found to have achieved greater predicted accuracy. In [38], a multilayer perceptron (MLP) and a radial basis function (RBF) were employed to model an UWB channel in an underground harsh environment. e training of the ANNs relied on 5% of the data as learning samples (8000 points out of 160010 points).…”

Section: Activation Functionsmentioning

confidence: 99%

Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis

Adebowale

Фарук

Adewole

et al. 2021

Mobile Information Systems

View full text Add to dashboard Cite

The importance of wireless path loss prediction and interference minimization studies in various environments cannot be over-emphasized. In fact, numerous researchers have done massive work on scrutinizing the effectiveness of existing path loss models for channel modeling. The difficulties experienced by the researchers determining or having the detailed information about the propagating environment prompted for the use of computational intelligence (CI) methods in the prediction of path loss. This paper presents a comprehensive and systematic literature review on the application of nature-inspired computational approaches in radio propagation analysis. In particular, we cover artificial neural networks (ANNs), fuzzy inference systems (FISs), swarm intelligence (SI), and other computational techniques. The main research trends and a general overview of the different research areas, open research issues, and future research directions are also presented in this paper. This review paper will serve as reference material for researchers in the field of channel modeling or radio propagation and in particular for research in path loss prediction.

show abstract

“…The other one is to predict the channel characteristics based on ML algorithms which can dig the mapping relationship between physical environment information and the channel characteristics. The function between frequency, distance, and path loss (PL) was modeled by two types of artificial neural networks (ANNs), i.e., multilayer perceptron (MLP) and radial basis function (RBF) [30][31][32][33][34]. In [35], PL was also modeled as a mapping relationship between delay and the atmosphere by MLP.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Predicting Wireless MmWave Massive MIMO Channel Characteristics Using Machine Learning Algorithms

Bai

Wang

Huang

et al. 2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

This paper proposes a procedure of predicting channel characteristics based on a well-known machine learning (ML) algorithm and convolutional neural network (CNN), for three-dimensional (3D) millimetre wave (mmWave) massive multiple-input multiple-output (MIMO) indoor channels. The channel parameters, such as amplitude, delay, azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA), are generated by a ray tracing software. After the data preprocessing, we can obtain the channel statistical characteristics (including expectations and spreads of the above-mentioned parameters) to train the CNN. The channel statistical characteristics of any subchannels in a specified indoor scenario can be predicted when the location information of the transmitter (Tx) antenna and receiver (Rx) antenna is input into the CNN trained by limited data. The predicted channel statistical characteristics can well fit the real channel statistical characteristics. The probability density functions (PDFs) of error square and root mean square errors (RMSEs) of channel statistical characteristics are also analyzed.

show abstract

Comparative experimental study on modeling the path loss of an UWB channel in a mine environment using MLP and RBF neural networks

Cited by 12 publications

References 12 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

Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis

Predicting Wireless MmWave Massive MIMO Channel Characteristics Using Machine Learning Algorithms

Contact Info

Product

Resources

About