Xiaoniu Yang

Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition

2022 · arXiv (Cornell University)

With the rapid development of deep learning, automatic modulation recognition (AMR), as an important task in cognitive radio, has gradually transformed from traditional feature extraction and classification to automatic classification by deep learning technology. However, …

EMD and VMD Empowered Deep Learning for Radio Modulation Recognition

2022 · IEEE Transactions on Cognitive Communications and Networking

Deep learning has been widely exploited in radio modulation recognition in recent years. In this paper, we exploit empirical mode decomposition (EMD) and variational mode decomposition (VMD) in deep learning-based radio modulation recognition. The received …

Residual CNN-Based Transceiver With Attention-Aided GAN for Unknown Channels

2025 · IEEE Transactions on Cognitive Communications and Networking

Intelligent transceivers and wireless channels form an autoencoder (AE) structure, demonstrating a significant improvement in communication performance through end-to-end (E2E) learning. Recently, when the wireless channel is unknown, a residual generative adversarial network (GAN) has …

A universal adversarial perturbations generation method based on feature aggregation

2024

Universal Adversarial Perturbations (UAP) is a sample-independent adversarial attack that can be added to all natural samples to change most of their predictive labels. Aiming at the problems of the existing universal adversarial attack methods …