  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.
