Abstract:

Automated Modulation Recognition （AMR） plays a critical role in wireless communications. A Denoising & Dual-modal Attention CNN-Transformer （D-DmACT） was proposed to address problems of poor transfer ability and insufficient abilities to distinguish noise and modulation signal features of AMR networks in complex electromagnetic environments. Firstly， a generator to generate complex electromagnetic interference iteratively and a discriminator to be against interference were proposed to enhance generalization ability of the network when encountering complex electromagnetic environments. Secondly， a complex attention-based Transformer module was designed to capture time-domain features of In-phase and Quadrature （IQ） signals， and a coordination attention module based on time-frequency information was proposed to acquire features of time-frequency images， then the features were crossed and fused. Thirdly， temporal sequence complex signals and time-frequency image obtained by the generator were sent to the dual-modal attention fusion model. Finally， lightweight classification and recognition were implemented. Experiments were conducted on datasets RadioML2016.10A and RadioML2018.01a under white Gaussian noise and complex electromagnetic environment， respectively. Experimental results with impulsive noise show that compared with CLDNN（Convolutional Long short-term Deep Neural Networks）， Residual Network （ResNet）， and LSTM（Long Short-Term Memory） Network， the proposed network has the average recognition accuracy increased by 53.98%， 28.82%， and 24.64%； compared with Multi-Modal approach toward AMR （MM-Net）， Threshold Autoencoder Denoiser Convolutional Neural Network （TADCNN）， and Generative Adversarial Network & Multi-modal Attention mechanism CNN-LSTM （GAN-MnACL）， the proposed network has the average recognition accuracy enhanced by 19.74%， 13.55% and 11.17%， respectively. In terms of computational complexity， deployability of the proposed network is validated through metrics such as parameters and FLoating point OPerations （FLOPs）.

Key words: Automated Modulation Recognition (AMR), Generative Adversarial Network (GAN), coordination attention mechanism, Transformer, complex jamming

摘要：

自动调制识别（AMR）是无线通信系统的关键技术。针对AMR网络在复杂电磁环境下迁移能力较差以及对噪声和调制信号的特征区分能力不足的问题，提出去噪及双模态注意力Transformer及卷积融合网络（Denoising & Dual-modal Attention CNN-Transformer， D-DmACT）。首先，设计一种迭代生成复杂干扰的生成器和对抗干扰的判别器，增强模型在遭遇复杂电磁环境时的泛化能力；其次，设计基于复数注意力的Transformer模块，以捕获同相正交（IQ）信号的时域特征，并设计基于时频位置信息的坐标注意力模块，以获取时频图像的特征，并对两种特征进行交叉融合；再次，将判别器输出的时序复序列和时频图像送至双模态注意力融合模型；最后，实现轻量化的分类识别。在数据集RadioML2016.10a和RadioML2018.01a上分别开展的高斯白噪声以及复杂电磁环境下的识别实验的结果表明：在脉冲噪声的作用下，相较于CLDNN（Convolutional Long short-term Deep Neural Network）、残差网络（ResNet）和长短期记忆（LSTM）网络，所提网络的平均识别准确率分别提高了53.98%、28.82%和24.64%，而相较于多模态自动调制分类网络（MM-Net）、阈值自编码去噪卷积神经网络（TADCNN）和生成式对抗网络联合多模态注意力机制卷积长短期记忆网络（GAN-MnACL），所提网络的平均识别准确率分别提高了19.74%、13.55%和11.17%。在计算复杂度方面，通过参数量和浮点运算数（FLOPs）等指标验证了所提网络在终端的可部署性。

关键词: 自动调制识别, 生成对抗网络, 坐标注意力机制, Transformer, 复杂干扰