  Accurate radio frequency power prediction in a geographic region is a
computationally expensive part of finding the optimal transmitter location
using a ray tracing software. We empirically analyze the viability of deep
learning models to speed up this process. Specifically, deep learning methods
including CNNs and UNET are typically used for segmentation, and can also be
employed in power prediction tasks. We consider a dataset that consists of
radio frequency power values for five different regions with four different
frame dimensions. We compare deep learning-based prediction models including
RadioUNET and four different variations of the UNET model for the power
prediction task. More complex UNET variations improve the model on higher
resolution frames such as 256x256. However, using the same models on lower
resolutions results in overfitting and simpler models perform better. Our
detailed numerical analysis shows that the deep learning models are effective
in power prediction and they are able to generalize well to the new regions.
