Determination of Neural Network Parameters for Path Loss Prediction in Very High Frequency Wireless Channel
Segun I. Popoola,Abigail Jefia,Aderemi A. Atayero,Ogbeide Kingsley,Nasir Faruk,Olasunkanmi F. Oseni,Robert O. Abolade +6 more
Reads0
Chats0
TLDR
An extensive investigation was conducted to determine the most appropriate neural network parameters for path loss prediction in Very High Frequency (VHF) band and showed that ANN-based path loss model has better prediction accuracy and generalization ability than the empirical models.Abstract:
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.read more
Citations
More filters
Journal ArticleDOI
Model-Aided Deep Learning Method for Path Loss Prediction in Mobile Communication Systems at 2.6 GHz
TL;DR: This paper compares traditional channel models to a channel model obtained using Deep Learning (DL)-techniques utilizing satellite images aided by a simple path loss model, and shows that the proposed DL model is capable of improving path loss prediction at unseen locations.
Journal ArticleDOI
RadioUNet: Fast Radio Map Estimation With Convolutional Neural Networks
TL;DR: It is shown that properly designed and trained deep neural networks are able to learn how to estimate the pathloss function, given an urban environment, in a very accurate and computationally efficient manner.
Journal ArticleDOI
Artificial Neural Network Based Path Loss Prediction for Wireless Communication Network
TL;DR: The most widely used multilayer perceptron (MLP) neural network in artificial neural network (ANN) is employed to accurately predict PL and three types of environmental features are defined and extracted, which describe the propagation environment only by considering limited environmental types instead of complex 3D environment modeling.
Journal ArticleDOI
An Overview of Machine Learning within Embedded and Mobile Devices-Optimizations and Applications.
TL;DR: A comprehensive overview of key application areas of EML technology is given, point out key research directions and highlight key take-away lessons for future research exploration in the embedded machine learning domain.
Journal ArticleDOI
Predicting Path Loss Distribution of an Area From Satellite Images Using Deep Learning
TL;DR: This paper presents a fundamentally different approach for path loss distribution prediction directly from 2D satellite images based on deep convolutional neural networks, and results show that the path losses can be accurately predicted for different communication frequencies and transmitter heights.
References
More filters
Journal ArticleDOI
Training feedforward networks with the Marquardt algorithm
TL;DR: The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks and is found to be much more efficient than either of the other techniques when the network contains no more than a few hundred weights.
Book
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
TL;DR: In this paper, Schnabel proposed a modular system of algorithms for unconstrained minimization and nonlinear equations, based on Newton's method for solving one equation in one unknown convergence of sequences of real numbers.
Book
Numerical methods for unconstrained optimization and nonlinear equations
TL;DR: Newton's Method for Nonlinear Equations and Unconstrained Minimization and methods for solving nonlinear least-squares problems with Special Structure.