Neural network-based path loss model for cellular mobile networks at 800 and 1800 MHz bands

Abstract

Cellular radio communication systems have become essential for data/voice/video/multimedia applications. The performance of cellular communication radio systems is assessed by considering the design specifications of frequency planning, channel assignment and interference mitigation strategies among others. Frequency planning is the most important component to improve capacity or quality of cellular radio systems. Large-scale path loss values between the base station and mobile stations are the key regulating factors that limit the performance of cellular systems, especially in urban/vegetation region. There is a necessity to develop a suitable path loss prediction model for predicting path loss values based on received signal strength measurements. In this paper, an ANN-based path loss model was used for macro cell measurement data obtained in the Vijayawada urban region, India. The Multi-Layer Perceptron (MLP) neural network model was considered. The prediction results indicate that the ANN model outperformed the Auto Regressive Moving Average (ARMA) and COST-231-WImodels. The outcome of this research work will be immensely useful for improving coverage and ensuring better frequency planning of cellular radio systems.