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

Anu Jagannath; Jithin Jagannath; Yanzhi Wang; Tommaso Melodia

doi:10.1117/12.2619125

31 May 2022 Deep neural network goes lighter: a case study of deep compression techniques on automatic RF modulation recognition for beyond 5G networks

Anu Jagannath, Jithin Jagannath, Yanzhi Wang, Tommaso Melodia

Author Affiliations +

Proceedings Volume 12097, Big Data IV: Learning, Analytics, and Applications; 1209708 (2022) https://doi.org/10.1117/12.2619125
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

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.

Conference Presentation

Citation Download Citation

Anu Jagannath, Jithin Jagannath, Yanzhi Wang, and Tommaso Melodia "Deep neural network goes lighter: a case study of deep compression techniques on automatic RF modulation recognition for beyond 5G networks", Proc. SPIE 12097, Big Data IV: Learning, Analytics, and Applications, 1209708 (31 May 2022); https://doi.org/10.1117/12.2619125

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available