DEVELOPMENT AND COMPARATIVE ANALYSIS OF QUOTIENTS REGRESSION BASED EMPIRICAL AND ARTIFICIAL NEURAL NETWORK BASED MODELS FOR PATH LOSS PREDICTION

📄 Project Abstract

The aim of this study is to develop and analytically compare Quotients Regression based empirical models with artificial neural network (ANN) based models for path loss prediction. The terrains considered as case study include i) the rural terrain between Jos and Abuja ii) the Urban terrain (Abuja), and iii) the semi-urban terrain (Maiduguri). The empirical models considered include the Okumura, Hata-Okumura, COST 231 Hata and the COST 231Walfisch-Ikegami, while the two types of ANN include the Multilayer Perceptron Neural Network (MLP-NN) and the Generalised Radial Basis Function Neural Network (GRBF-NN). A Quotients Regression Technique (QRT) for empirical model adaptation was developed and used to selectively adapt these empirical models to the terrains, based on path loss measurements obtained from Base Transceiver Stations (BTS) situated within the terrains. The adaptation accuracy of the QRT was determined through comparisons with two existing adaptation techniques: i) The Okumur...