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.

中文翻译：

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基于DL的大规模MIMO CSI反馈的对抗性攻击

随着深度学习（DL）算法在无线通信中的应用越来越多，物理层面临着对抗性攻击带来的新挑战。这种攻击已经严重影响了计算机视觉中的神经网络。我们选择基于 DL 的信道状态信息 (CSI) 来展示对抗性攻击对基于 DL 的通信系统的影响。我们提出了一种实用的方法来对基于 DL 的 CSI 反馈过程进行白盒对抗攻击。我们的仿真结果通过分析归一化均方误差的性能，展示了对抗性攻击对基于 DL 的 CSI 反馈造成的破坏性影响。我们还发起了干扰攻击进行比较，发现可以通过一定的预防措施来防止干扰攻击。随着DL算法成为发展无线通信的趋势，