Robust Massive MIMO Localization Using Neural ODE in Adversarial Environments

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.