Efficient Spatial-Temporal Angle-Delay Analysis Scheme for Massive MIMO Indoor Tracking

Radio positioning is critical for many indoor applications, such as behavioral monitoring and autonomous robots. Mobile users, however, can also be exposed to surveillance risks due to this capability. This work presents a Spatial-Temporal Angle-Delay Analysis Scheme (STADAS) for massive MIMO wireless networks that can help the attacker to track a user without the need to enter buildings. First, we transform the channel state information (e.g., angle of arrival, time of arrival) from massive MIMO transmission gained over time into living Angle-Delay profiles (ADPs) with fixed objects (building walls, furniture) and a moving object (the mobile user). Second, a generative adversarial network learning model is used to remove distorted data points from Angle-Delay video frames. The processed ADPs are trained with a Deep Convolutional Neural Network (DCNN)-based model on estimating the user's location. Evaluations on an empirical dataset indicate that radio positioning capabilities in emerging wireless communication technologies such as mmWave MIMO can pose severe privacy and surveillance threats.