IDAF: Iterative Dual-Scale Attentional Fusion Network for Automatic Modulation Recognition

Recently, deep learning models have been widely applied to modulation recognition, and they have become a hot topic due to their excellent end-to-end learning capabilities. However, current methods are mostly based on uni-modal inputs, which suffer from incomplete information and local optimization....

Full description

Saved in:

Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Switzerland), 2023-09, Vol.23 (19), p.8134
Main Authors:	Liu, Bohan, Ge, Ruixing, Zhu, Yuxuan, Zhang, Bolin, Zhang, Xiaokai, Bao, Yanfei
Format:	Article
Language:	eng
Subjects:	Accuracy attention mechanism automatic modulation recognition convolutional neural network Data transmission Deep learning Hypothesis testing multimodal learning Neural networks Signal to noise ratio
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Recently, deep learning models have been widely applied to modulation recognition, and they have become a hot topic due to their excellent end-to-end learning capabilities. However, current methods are mostly based on uni-modal inputs, which suffer from incomplete information and local optimization. To complement the advantages of different modalities, we focus on the multi-modal fusion method. Therefore, we introduce an iterative dual-scale attentional fusion (iDAF) method to integrate multimodal data. Firstly, two feature maps with different receptive field sizes are constructed using local and global embedding layers. Secondly, the feature inputs are iterated into the iterative dual-channel attention module (iDCAM), where the two branches capture the details of high-level features and the global weights of each modal channel, respectively. The iDAF not only extracts the recognition characteristics of each of the specific domains, but also complements the strengths of different modalities to obtain a fruitful view. Our iDAF achieves a recognition accuracy of 93.5% at 10 dB and 0.6232 at full signal-to-noise ratio (SNR). The comparative experiments and ablation studies effectively demonstrate the effectiveness and superiority of the iDAF.
ISSN:	1424-8220 1424-8220