Robust Massive MIMO Localization Using Neural ODE in Adversarial Environments
Robust Massive MIMO Localization Using Neural ODE in Adversarial Environments
复制标题
DOI：
10.1109/icc45855.2022.9838836
复制
发表时间：
2022-05
期刊：
影响因子：
--
通讯作者：
Ushasree Boora;Xuyu Wang;S. Mao
中科院分区：
文献类型：
--
作者：
Ushasree Boora;Xuyu Wang;S. Mao
随着5G通信系统的广泛部署，5G大规模多输入多输出(MIMO)不仅可以有效地提高频谱效率和能量效率，还可以提供基于位置的服务(LBS)，如室外车辆定位和室内用户定位。近年来，深度卷积神经网络(DCNN)已被应用于利用信道状态信息(CSI)或角度延迟分布(ADP)进行大规模MIMO定位。然而，在大规模MIMO定位中，DCNN模型的稳健性还没有被探索。本文利用DCNN和神经常微分方程(ODE)模型研究了对抗性攻防(即对抗性训练)对大规模MIMO定位的影响。首先介绍了海量MIMO系统的信道模型和ADP指纹，然后给出了海量MIMO定位的DCNN模型和神经ODE模型，以及白盒对抗攻击和对抗训练的三种类型。最后，我们的实验结果验证了所提出的对抗性训练的神经ODE能够有效地提高室内和室外环境下大规模MIMO定位的鲁棒性。
With the wide deployment of 5G communication systems, 5G massive multiple-input multiple-output (MIMO) has been shown effective not only to improve the spectrum efficiency and energy efficiency, but also provides location-based service (LBS) such as outdoor vehicle localization and indoor user localization. Recently, deep convolutional neural network (DCNN) has been applied for massive MIMO localization using channel state information (CSI) or angle-delay profile (ADP). However, the robustness of the DCNN model has not been explored in massive MIMO localization. In this paper, we study the impact of adversarial attack and defense (i.e., adversarial training) on massive MIMO localization using DCNN and the neural ordinary differential equation (ODE) model. We first introduce the massive MIMO system with respect to the channel model and ADP fingerprints, and then present the DCNN model and the neural ODE model for massive MIMO localization, as well as three types of white-box adversarial attacks and adversarial training. Finally, our experimental results validate that the proposed neural ODE with adversarial training could effectively improve the robustness of massive MIMO localization in indoor and outdoor environments.