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DEEP LEARNING-BASED PATH LOSS PREDICTION FOR OUTDOOR WIRELESS COMMUNICATION SYSTEMS

Kehai Qiu (University of Cambridge); Stefanos Bakirtzis (University of Cambridge); Hui Song (Ranplan Wireless Network Design Ltd); Ian J Wassell (University of Cambridge); Jie Zhang (University of Sheffield)

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10 Jun 2023

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 trans- form information related to an outdoor propagation environment to 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.