3 papers
eess.SP2025
Demonstrating Interoperable Channel State Feedback Compression with Machine Learning
Dani Korpi, Rachel Wang, Jerry Wang +20
Neural network-based compression and decompression of channel state feedback has been one of the most widely studied applications of machine learning (ML) in wireless networks. Var…
cs.CR2025
EveGuard: Defeating Vibration-based Side-Channel Eavesdropping with Audio Adversarial Perturbations
Jung-Woo Chang, Ke Sun, David Xia +2
Vibrometry-based side channels pose a significant privacy risk, exploiting sensors like mmWave radars, light sensors, and accelerometers to detect vibrations from sound sources or…
cs.CR2024
Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems
Jung-Woo Chang, Ke Sun, Nasimeh Heydaribeni +3
Machine Learning (ML) has been instrumental in enabling joint transceiver optimization by merging all physical layer blocks of the end-to-end wireless communication systems. Althou…