With the rapid development of deep learning (DL) in recent years, automatic modulation recognition (AMR) with DL has achieved high accuracy. However, insufficient training signal data in complicated channel environments and large-scale DL models are critical factors that make DL methods difficult to deploy in practice. Aiming to these problems, we propose a novel neural network named convolution-linked signal transformer (ClST) and a novel knowledge distillation method named signal knowledge distillation (SKD). The ClST is accomplished through three primary modifications: a hierarchy of transformer containing convolution, a novel attention mechanism named parallel spatial-channel attention (PSCA) mechanism and a novel convolutional transformer block named convolution-transformer projection (CTP) to leverage a convolutional projection. The SKD is a knowledge distillation method to effectively reduce the parameters and complexity of neural networks. We train two lightweight neural networks using the SKD algorithm, KD-CNN and KD-MobileNet, to meet the demand that neural networks can be used on miniaturized devices. The simulation results demonstrate that the ClST outperforms advanced neural networks on all datasets. Moreover, both KD-CNN and KD-MobileNet obtain higher recognition accuracy with less network complexity, which is very beneficial for the deployment of AMR on miniaturized communication devices.

翻译：近年来，随着深度学习的快速发展，基于深度学习的自动调制识别已实现高精度。然而，复杂信道环境下训练信号数据的不足以及大规模深度学习模型，是导致深度学习方法难以实际部署的关键因素。针对这些问题，我们提出一种名为卷积链接信号变换器（ClST）的新型神经网络，以及一种名为信号知识蒸馏（SKD）的新型知识蒸馏方法。ClST通过三项主要改进实现：包含卷积的Transformer层级结构、名为并行空间-通道注意力（PSCA）机制的新型注意力机制，以及利用卷积投影的新型卷积Transformer模块——卷积变换投影（CTP）。SKD是一种有效减少神经网络参数和复杂度的知识蒸馏方法。我们使用SKD算法训练了两种轻量级神经网络——KD-CNN和KD-MobileNet，以满足神经网络在微型化设备上使用的需求。仿真结果表明，ClST在所有数据集上均优于先进神经网络。此外，KD-CNN和KD-MobileNet在保持更低网络复杂度的同时实现了更高识别准确率，这对自动调制识别在微型化通信设备上的部署极为有利。