Augmenting Radio Signals with Wavelet Transform for Deep Learning-Based Modulation Recognition

TL;DR

This paper introduces wavelet transform-based data augmentation techniques to enhance deep learning models for radio modulation recognition, improving classification accuracy especially when training data is limited.

Contribution

It proposes novel augmentation methods using wavelet detail coefficients to generate diverse training samples for radio signal classification.

Findings

Proposed methods outperform existing augmentation techniques.

Significant improvement in modulation recognition accuracy.

Effective in scenarios with limited training data.

Abstract

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 data augmentation methods that involve replacing detail coefficients decomposed by discrete wavelet transform for reconstructing to generate new samples and expand 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.