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Augmenting Radio Signals With Wavelet Transform for Deep Learning-Based Modulation Recognition
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DOI：10.1109/TCCN.2024.3400525.png)
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Abstract
En 中文
The use of deep learning for radio modulation recognition has become prevalent in recent years. This approach automatically extracts high-dimensional features from large datasets, facilitating the accurate classification of modulation schemes. However, in real-world scenarios, it may not be feasible to gather sufficient training data in advance. Data augmentation is a method used to increase the diversity and quantity of training dataset and to reduce data sparsity and imbalance. In this paper, we propose a data augmentation method that applies wavelet transform for the first time in the field of data augmentation. This method involves replacing detail coefficients decomposed by discrete wavelet transform to reconstruct and generate new samples using different wavelet bases, thereby expanding the training set. Different generation methods are used to generate replacement sequences. Simulation results indicate that our proposed methods significantly outperform the other augmentation methods.
Keywords:
Modulation
Feature extraction
Data augmentation
Training
Deep learning
Task analysis
Training data
Radio modulation recognition
deep learning
data augmentation
discrete wavelet transform
convolutional neural networks
Journal
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7
Papers:
1.5K
Citations:
5.5K