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BacalhauNet: A tiny CNN for lightning-fast modulation classification
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Authors: Jose Rosa, Daniel Granhao, Guilherme Carvalho, Tiago Gonçalves, Monica Figueiredo, Luis Conde Bento, Nuno Paulino, Luis M. Pessoa
Status: Final
Date of publication: 22 September 2022
Published in: ITU Journal on Future and Evolving Technologies, Volume 3 (2022), Issue 2, Pages 252-260
Article DOI : https://doi.org/10.52953/FYWT4006
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Abstract:
Deep learning methods have been shown to be competitive solutions for modulation classification tasks, but suffer from being computationally expensive, limiting their use on embedded devices. We propose a new deep neural network architecture which employs known structures, depth-wise separable convolution and residual connections, as well as a compression methodology, which combined lead to a tiny and fast algorithm for modulation classification. Our compressed model won the first place in ITU's AI/ML in 5G Challenge 2021, achieving 61.73× compression over the challenge baseline and being over 2.6× better than the second best submission. The source code of this work is publicly available at github.com/ITU-AI- ML-in-5G-Challenge/ITU-ML5G-PS-007-BacalhauNet. |
Keywords: 5G, deep neural networks, machine learning, modulation classification, model compression
Rights: © International Telecommunication Union, available under the CC BY-NC-ND 3.0 IGO license.
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