User Location Tracking in Massive MIMO Systems via Dynamic Variational Bayesian Inference

Accurate user location tracking is the key to enable location-based services and assist communications in 5G networks. The massive multiple-input multiple-output (MIMO) technology employed in 5 G networks could potentially provide accurate user localization due to increased spectral efficiency and high directivity. In this paper, we propose an efficient user location tracking algorithm in massive MIMO systems. Firstly, we propose a temporal Markov group-sparse (TMGS) model based on a grid reference to capture the probabilistic temporal correlation and group sparsity of the massive MIMO channels jointly. Then we propose a dynamic variational Bayesian inference (D-VBI) algorithm to handle the TMGS priors under ill-conditioned measurement matrix in the location tracking problem. The proposed D-VBI can jointly recover the user's coarse location in the grid reference and refine the off-grid points to automatically learn the user's exact location to high accuracy. Moreover, the TMGS-based D-VBI algorithm can provide prior information about the user's next location and the possible arriving directions of the future channels to the consecutive time slot to improve the location tracking accuracy. Finally, we verify the superior performance of the proposed location tracking algorithm by extensive simulations.