Abstract
The aim of this paper is to present and evaluate artificial neural network model used for path loss prediction of signal propagation in the VHF/UHF spectrum in Edo state.Measurement data obtained from three television broadcasting stations in Edo state, operating at 189.25MHz, 479.25MHz, and 743.25MHz, is used to train and evaluate the artificial neural network. A two layer neural network with one hidden and one output layer is evaluated regarding prediction accuracy and generalization properties. The path loss prediction results obtained by using the artificial neural network model are evaluated against the Hata and Walfisch-Ikegami empirical path loss models .Result analysis shows that the artificial neural network performs well as regards to prediction accuracy and generalization ability. The ANN performed better across all performance measures in comparison to the Hata and Walfisch-Ikegami and Line of Sight models in estimating path loss in vhf/uhf spectrum in Edo state.
Highlights
Path loss is a major component in the analysis and design of the link budget of a telecommunication system
Artificial neural networks (ANNs) have been proposed in order to obtain prediction models that are more accurate than standard empirical models whilst being easier to compute than deterministic models
81 predictive ability of the neural networks and the empirical models used in this paper include; Mean Squared Error (MSE); Root mean square error or root mean square deviation (RMSE or RMSD), Maximum error Deviation, Coefficient of Determination (R-squared)
Summary
Path loss is a major component in the analysis and design of the link budget of a telecommunication system. Path loss prediction models have been based on empirical and/or deterministic methods. Artificial neural networks (ANNs) have been proposed in order to obtain prediction models that are more accurate than standard empirical models whilst being easier to compute than deterministic models. An Artificial Neural Network (ANN) prediction model can be trained to perform well in environments similar to where the training data is collected. A multilayer feed-forward network with back-propagation can generally be used to solve function fitting or function approximation problems This approximation ability can be applied to path loss prediction. The Free Space loss model only takes into consideration distance and frequency This model is very limited in its ability to accurately predict path loss in most environments [8]
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