Specific Attenuation Estimation in Perturbed Radio Wave Propagation Using Artificial Neural Networks

Abstract

Path loss modeling is a crucial consideration in radio engineering for wireless networks. Over the years, diverse techniques have been implemented in attempts to accurately predict path loss across a given terrain. In this study, path loss predictors created on the bases of artificial neural networks (ANN) were used to estimate path loss across a rural section of the Nigerian middle-belt grassland. The ANN structures considered were the Generalized Regression Neural network (GRNN) and the Radial Basis Function Neural Network (RBFNN), which exhibit a few differences and similarities. These ANN based predictors were trained, validated and tested for path loss prediction using path loss values computed from received power measured at 900MHz from six Base Transceiver Stations (BTSs) situated along the rural terrain. Findings show that the RBFNN predictor with a Root Mean Squared Error (RMSE) of 5.17dB and the GRNN with 4.9dB are slightly more accurate than the COST 231 Hata model with 6.64dB, while the Hata-Okumura with 25.78dB is simply not suitable for the terrain under investigation. Overall, the GRNN, which proffers a 26.21% improvement over the COST 231 Hata is recommended for the terrain in question.