An empirical study on using CNNs for fast radio signal prediction

TL;DR

This study evaluates the effectiveness of CNNs and UNET models in rapidly predicting radio frequency power across different regions, aiming to reduce computational costs in transmitter placement optimization.

Contribution

It provides an empirical comparison of various UNET-based models for radio power prediction, highlighting their performance differences across resolutions and regions.

Findings

Deep learning models effectively predict radio power.

Higher resolution models perform better with complex UNET variations.

Simpler models are preferable for lower resolution data.

Abstract

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.