The recent advancement in deep learning (DL) for automatic modulation
classification (AMC) of wireless signals has encouraged numerous possible
applications on resource-constrained edge devices. However, developing
optimized DL models suitable for edge applications of wireless communications
is yet to be studied in depth. In this work, we perform a thorough
investigation of optimized convolutional neural networks (CNNs) developed for
AMC using the three most commonly used model optimization techniques: a)
pruning, b) quantization, and c) knowledge distillation. Furthermore, we have
proposed optimized models with the combinations of these techniques to fuse the
complementary optimization benefits. The performances of all the proposed
methods are evaluated in terms of sparsity, storage compression for network
parameters, and the effect on classification accuracy with a reduction in
parameters. The experimental results show that the proposed individual and
combined optimization techniques are highly effective for developing models
with significantly less complexity while maintaining or even improving
classification performance compared to the benchmark CNNs.

深度学习在自动调制分类方面的最近进展鼓舞了许多可能应用于资源受限的边缘设备上的无线信号处理，本文通过对三种最常用的模型优化技术（剪枝、量化和知识蒸馏）进行了深入研究，提出了优化的卷积神经网络结构以实现自动调制分类。实验结果表明，所提出的单独和结合的优化技术对于以显著较小的复杂性开发模型，并在减少参数的同时维持或提高分类性能方面是非常有效的。

边缘高效的深度学习模型用于自动调制分类：性能分析

Edge-Efficient Deep Learning Models for Automatic Modulation  Classification: A Performance Analysis

We study efficient deep learning training algorithms that process received
wireless signals, if a test Signal to Noise Ratio (SNR) estimate is available.
We focus on two tasks that facilitate source identification: 1- Identifying the
modulation type, 2- Identifying the wireless technology and channel in the 2.4
GHz ISM band. For benchmarking, we rely on recent literature on testing deep
learning algorithms against two well-known datasets. We first demonstrate that
using training data corresponding only to the test SNR value leads to dramatic
reductions in training time while incurring a small loss in average test
accuracy, as it improves the accuracy for low SNR values. Further, we show that
an erroneous test SNR estimate with a small positive offset is better for
training than another having the same error magnitude with a negative offset.
Secondly, we introduce a greedy training SNR Boosting algorithm that leads to
uniform improvement in accuracy across all tested SNR values, while using a
small subset of training SNR values at each test SNR. Finally, we demonstrate
the potential of bootstrap aggregating (Bagging) based on training SNR values
to improve generalization at low test SNR values with scarcity of training
data.

本研究旨在探讨利用深度学习处理接收到的无线信号，并通过测试信噪比估计实现源识别，主要集中在识别调制类型和无线技术以及信道。研究表明通过利用测试信噪比对应的训练数据进行训练能够显著减少训练时间而保留一定的平均测试准确度，同时，以小正偏移为误差的测试信噪比估计比以小负偏移为误差的估计更有利于训练。另外，我们提出了一种贪婪的训练信噪比提升算法，可在使用每个测试信噪比的小部分训练信噪比的情况下，在所有测试信噪比的准确度上均匀提高。最后，通过基于训练信噪比值的自举聚集算法来改善具有训练数据紧缺的低测试信噪比值的泛化能力的潜力得到了验证。