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。

深度神经网络变轻：基于深度压缩技术的自动射频调制识别在 5G 及其后网络中的应用研究

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

Neural networks are both computationally intensive and memory intensive,
making them difficult to deploy on embedded systems with limited hardware
resources. To address this limitation, we introduce "deep compression", a three
stage pipeline: pruning, trained quantization and Huffman coding, that work
together to reduce the storage requirement of neural networks by 35x to 49x
without affecting their accuracy. Our method first prunes the network by
learning only the important connections. Next, we quantize the weights to
enforce weight sharing, finally, we apply Huffman coding. After the first two
steps we retrain the network to fine tune the remaining connections and the
quantized centroids. Pruning, reduces the number of connections by 9x to 13x;
Quantization then reduces the number of bits that represent each connection
from 32 to 5. On the ImageNet dataset, our method reduced the storage required
by AlexNet by 35x, from 240MB to 6.9MB, without loss of accuracy. Our method
reduced the size of VGG-16 by 49x from 552MB to 11.3MB, again with no loss of
accuracy. This allows fitting the model into on-chip SRAM cache rather than
off-chip DRAM memory. Our compression method also facilitates the use of
complex neural networks in mobile applications where application size and
download bandwidth are constrained. Benchmarked on CPU, GPU and mobile GPU,
compressed network has 3x to 4x layerwise speedup and 3x to 7x better energy
efficiency.

该论文介绍了一种名为 “深度压缩” 的技术，通过三阶段的网络压缩流程 —— 剪枝、量化和霍夫曼编码 —— 可以将神经网络模型的存储资源需求减小 35 到 49 倍，而同时不影响网络的准确性，该技术可以在嵌入式系统中使用，可以极大地提升模型的应用性能。