Undermining Deep Learning Based Channel Estimation via Adversarial Wireless Signal Fabrication

Channel estimation is a crucial step in wireless communications. The estimator identi.es the wireless channel distortions during the signal propagation and this information is further used for data precoding and decoding. Recent studies have shown that deep learning techniques can enhance the accuracy of conventional channel estimation algorithms. However, the reliability and security aspects of these deep learning algorithms have not yet been well investigated in the context of wireless communications. With no exceptions, channel estimation based on deep learning may be vulnerable to the adversarial machine learning attacks. However, close examination shows that we cannot simply adapt the traditional adversarial learning mechanisms to e.ectively manipulate channel estimation. In this paper, we propose a novel attack strategy that crafts a perturbation to fool the receiver with wrong channel estimation results. This attack is launched without knowing the current input signals and by only requiring a loose form of time synchronization. Through the over-the-air experiments with software-de.ned radios in our multi-user MIMO testbed, we show that the proposed strategy can e.ectively reduce the performance of deep learning-based channel estimation. We also demonstrate that the proposed attack can hardly be detected with the detection rate of 8% or lower.