Deep neural network goes lighter: A case study of deep compression techniques on automatic RF modulation recognition for Beyond 5G networks

• URL: http://arxiv.org/abs/2204.04390v1
• Date: Sat, 9 Apr 2022 04:51:26 GMT
• Title: Deep neural network goes lighter: A case study of deep compression techniques on automatic RF modulation recognition for Beyond 5G networks
• Authors: Anu Jagannath, Jithin Jagannath, Yanzhi Wang, and Tommaso Melodia
• Abstract summary: This letter provides an in-depth view of the state-of-the-art deep compression and acceleration techniques for automatic RF modulation recognition. Lightweight neural networks are key to sustain edge computation capability on resource-constrained platforms.
• Score: 34.71271274267469
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Automatic RF modulation recognition is a primary signal intelligence (SIGINT) technique that serves as a physical layer authentication enabler and automated signal processing scheme for the beyond 5G and military networks. Most existing works rely on adopting deep neural network architectures to enable RF modulation recognition. The application of deep compression for the wireless domain, especially automatic RF modulation classification, is still in its infancy. Lightweight neural networks are key to sustain edge computation capability on resource-constrained platforms. In this letter, we provide an in-depth view of the state-of-the-art deep compression and acceleration techniques with an emphasis on edge deployment for beyond 5G networks. Finally, we present an extensive analysis of the representative acceleration approaches as a case study on automatic radar modulation classification and evaluate them in terms of the computational metrics.

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