  Pathloss prediction is an essential component of wireless network planning.
While ray tracing based methods have been successfully used for many years,
they require significant computational effort that may become prohibitive with
the increased network densification and/or use of higher frequencies in 5G/B5G
(beyond 5G) systems. In this paper, we propose and evaluate a data-driven and
model-free pathloss prediction method, dubbed PMNet. This method uses a
supervised learning approach: training a neural network (NN) with a limited
amount of ray tracing (or channel measurement) data and map data and then
predicting the pathloss over location with no ray tracing data with a high
level of accuracy. Our proposed pathloss map prediction-oriented NN
architecture, which is empowered by state-of-the-art computer vision
techniques, outperforms other architectures that have been previously proposed
(e.g., UNet, RadioUNet) in terms of accuracy while showing generalization
capability. Moreover, PMNet trained on a 4-fold smaller dataset surpasses the
other baselines (trained on a 4-fold larger dataset), corroborating the
potential of PMNet.
