.png){kind=icon}
返回
Efficient convolutional dual-attention transformer for automatic modulation recognition
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-024-06202-6.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Automatic modulation recognition (AMR) involves identifying the modulation of electromagnetic signals in a noncollaborative manner. Deep learning-based methods have become a focused research topic in the AMR field. Such models are frequently trained using standardized data, relying on many computational and storage resources. However, in real-world applications, the finite resources of edge devices limit the deployment of large-scale models. In addition, traditional networks cannot handle real-world signals of varying lengths and local missing data. Thus, we propose a network structure based on a convolutional Transformer with a dual-attention mechanism. This proposed structure effectively utilizes the inductive bias of the lightweight convolution and the global property of the Transformer model, thereby fusing local features with global features to get high recognition accuracy. Moreover, the model can adapt to the length of the input signals while maintaining strong robustness against incomplete signals. Experimental results on the open-source datasets RML2016.10a, RML2016.10b, and RML2018.01a demonstrate that the proposed network structure can achieve 95.05%, 94.79%, and 98.14% accuracy, respectively, with enhancement training and maintain greater than 90% accuracy when the signals are incomplete. In addition, the proposed network structure has fewer parameters and lower computational cost than benchmark methods.
Keyword:
Efficient modulation recognition
Lightweight convolution
Dual-attention mechanism
Transformer
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
机构
引用论文
.png){kind=icon}
A Transformer-Based Contrastive Semi-Supervised Learning Framework for Automatic Modulation Recognition基于Transformer的对比半监督学习自动调制识别框架
PREAI
分享收藏Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors基于分布式低成本频谱传感器的无线信号分类深度学习模型
OAAI
OAAI分享收藏Metal-organic chemical vapor deposition of indium selenide films using a single-source precursor使用单源前体对硒化铟薄膜进行金属有机化学气相沉积
PREAI
分享收藏
查看更多内容