An empirical study on using CNNs for fast radio signal prediction
This work addresses the need for faster radio signal prediction in transmitter placement, but it is incremental as it applies existing deep learning methods to a specific domain.
The study tackled the problem of computationally expensive radio frequency power prediction by empirically analyzing deep learning models like CNNs and UNET, finding that they are effective and generalize well to new regions, with more complex UNET variations improving performance on higher resolution frames like 256x256.
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.