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

• URL: http://arxiv.org/abs/2311.03761v1
• Date: Tue, 7 Nov 2023 06:55:39 GMT
• Title: Augmenting Radio Signals with Wavelet Transform for Deep Learning-Based Modulation Recognition
• Authors: Tao Chen, Shilian Zheng, Kunfeng Qiu, Luxin Zhang, Qi Xuan, and Xiaoniu Yang
• Abstract summary: Deep learning for radio modulation recognition has become prevalent in recent years. 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.
• Score: 6.793444383222236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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