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.

本文主要介绍了一种自动无线电调制识别技术，结合深度压缩和加速技术提出了一种轻量神经网络模型，在边缘侧设备上可以实现物理层身份认证，自动信号处理，为未来 5G 及军用网络提供技术支持。同时，以自动雷达调制调制分类为例，对代表性加速方法进行了广泛分析并进行了计算指标衡量。