Deep Learning-Based Path Loss Prediction for Outdoor Wireless Communication Systems
Kehai Qiu, Stefanos Bakirtzis, Hui Song, Ian Wassell, Jie Zhang
Abstract
Deep learning (DL) has been recently leveraged for the inference of characteristics related to wireless communication channels, such as path loss (PL). This paper presents how a deep convolutional encoder-decoder, namely a path loss prediction net (PPNet) based on SegNet, can be trained to transform information related to an outdoor propagation environment into a PL heatmap. This work is a part of the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing First Pathloss Radio Map Prediction Challenge. The DL model is trained with synthetic data generated with a high-performance ray tracing simulator and it is illustrated that PPNet can indeed learn to predict the PL distribution and that it generalizes well to previously unseen outdoor propagation environments.