Abstract: This paper presents a data-driven localization framework with high precision
in time-varying complex multipath environments, such as dense urban areas and
indoors, where GPS and model-based localization techniques come short. We
consider the angle-delay profile (ADP), a linear transformation of channel
state information (CSI), in massive MIMO systems and show that ADPs preserve
users' motion when stacked temporally. We discuss that given a static
environment, future frames of ADP time-series are predictable employing a video
frame prediction algorithm. We express that a deep convolutional neural network
(DCNN) can be employed to learn the background static scattering environment.
To detect foreground changes in the environment, corresponding to path blockage
or addition, we introduce an algorithm taking advantage of the trained DCNN.
Furthermore, we present DyLoc, a data-driven framework to recover distorted
ADPs due to foreground changes and to obtain precise location estimations. We
evaluate the performance of DyLoc in several dynamic scenarios employing
DeepMIMO dataset to generate geo-tagged CSI datasets for indoor and outdoor
environments. We show that previous DCNN-based techniques fail to perform with
desirable accuracy in dynamic environments, while DyLoc pursues localization
precisely. Moreover, simulations show that as the environment gets richer in
terms of the number of multipath, DyLoc gets more robust to foreground changes.