Path loss exponent and shadowing factor prediction from satellite images using deep learning

Abstract

Optimal network planning for wireless communication systems requires the detailed knowledge of the channel parameters of the target coverage area. Channel parameters can be estimated through extensive measurements in the environment. Alternatively, ray tracing simulations can be done if the 3D model of the environment is available. One drawback of ray tracing simulations is the high computational complexity; therefore, ray tracing is not suitable for real-time coverage optimization. In this paper, we present a deep convolutional neural network-based approach to estimate channel parameters (specifically, path loss exponent and standard deviation of shadowing) directly from 2D satellite images. While deep learning methods require high computational resources for training and large amount of training data, once trained, the network can make predictions fast. Also, unlike the ray tracing simulations, there is no need for 3D model generation, and therefore, it can be applied easily using the images obtained from satellites or aerial vehicles. These make the proposed method a computationally efficient and reliable alternative to ray tracing simulations. The experimental results show that path loss exponent and large-scale shadowing factor at 900 MHz can be correctly classified by 88% and 76% accuracy, respectively.