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An Empirical Study on Using CNNs for Fast Radio Signal Prediction

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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 \(256\times 256\). 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.

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Notes

  1. https://radiomapseer.github.io/.

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Acknowledgements

This research is in part supported by Communications Research Centre (CRC) Canada.

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Authors and Affiliations

  1. Mechanical Industrial Engineering Department, Ryerson University, Toronto, ON, M5B 2K3, Canada

    Ozan Ozyegen, Sanaz Mohammadjafari, Mucahit Cevik, Karim El mokhtari & Ayse Basar

  2. Communications Research Centre Canada, Ottawa, Canada

    Jonathan Ethier

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  1. Ozan Ozyegen
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Correspondence to Mucahit Cevik.

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Ozyegen, O., Mohammadjafari, S., Cevik, M. et al. An Empirical Study on Using CNNs for Fast Radio Signal Prediction. SN COMPUT. SCI. 3, 131 (2022). https://doi.org/10.1007/s42979-022-01022-2

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