Adversarial attack on DL-based massive MIMO CSI feedback
Qing Liu, Jiajia Guo, Chao-Kai Wen, Shi Jin
Abstract
With the increasing application of deep learning (DL) algorithms in wireless communications, the physical layer faces new challenges caused by adversarial attack. Such attack has significantly affected the neural network in computer vision. We choose DL-based channel state information (CSI) to show the effect of adversarial attack on DL-based communication system. We present a practical method to craft white-box adversarial attack on DL-based CSI feedback process. Our simulation results show the destructive effect adversarial attack causes on DL-based CSI feedback by analyzing the performance of normalized mean square error. We also launch a jamming attack for comparison and find that the jamming attack could be prevented with certain precautions. As DL algorithm becomes the trend in developing wireless communication, this work raises concerns regarding the security in the use of DL-based algorithms.