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.
Highlights
Wireless communication is fast evolving with diverse disruptive enabling technologies, thereby leading to a serious demand for high quality signal strength and larger network capacity [1]
Popoola et al.: Determination of Neural Network Parameters for Path Loss Prediction efficient network design to be achieved, accurate and reliable path loss models are highly essential for radio network coverage and signal interference predictions
Artificial Neural Network (ANN) can be adapted for path loss predictions in rural, suburban and urban propagation environments
Summary
Wireless communication is fast evolving with diverse disruptive enabling technologies, thereby leading to a serious demand for high quality signal strength and larger network capacity [1]. Wireless connectivity and coverage required for sustainable digital transformation is still not. Globally available; and this poses a challenge to the timely accomplishment of the desired SDGs. globally available; and this poses a challenge to the timely accomplishment of the desired SDGs With this in mind, it is evident that the extension of the wireless connectivity and coverage to the yet-to-be-reached population would facilitate global digital transformation, providing the necessary technology required for the development of ICT services in the underserved areas. In a bid to design efficient wireless communication systems, the propagation factors affecting the radio channel often pose serious challenges to radio network engineers whose responsibility is to ensure that subscribers are provided with high speed Internet services at optimum Received Signal Strength (RSS) [3].
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