A Machine Learning Based 3D Propagation Model for Intelligent Future Cellular Networks

Abstract

In modern wireless communication systems, radio propagation modeling has always been a fundamental task in system design and performance optimization. These models are used in cellular networks and other radio systems to estimate the pathloss or the received signal strength (RSS) at the receiver or characterize the environment traversed by the signal. An accurate and agile estimation of pathloss is imperative for achieving desired optimization objectives. The state-of-the- art empirical propagation models are based on measurements in a specific environment and limited in their ability to capture idiosyncrasies of various propagation environments. To cope with this problem, ray-tracing based solutions are used in commercial planning tools, but they tend to be extremely time consuming and expensive. In this paper, we propose a Machine Learning (ML) based approach to complement the empirical or ray tracing-based models, for radio wave propagation modeling and RSS estimation. The proposed ML-based model leverages a pre-identified set of smart predictors, including transmitter parameters and the physical and geometric characteristics of the propagation environment, for estimating the RSS. These smart predictors are readily available at the network-side and need no further standardization. We have quantitatively compared the performance of several machine learning algorithms in their ability to capture the channel characteristics, even with sparse availability of training data. Our results show that Deep Neural Networks outperforms other ML techniques and provides a 25% increase in prediction accuracy as compared to state-of-the-art empirical models and a 12x decrease in prediction time as compared to ray tracing.