Bayesian Channel Tracking and AoA Acquisition in Millimeter Wave MIMO Systems with Low-Resolution ADCs

This paper considers the channel tracking and angle of arrival (AoA) acquisition for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, in which each antenna at the base station is equipped with low-resolution analog-to-digital converters (ADCs) to quantize the received signals. We utilize the beamspace Gauss-Markov model to capture the sparsity and temporal correlation of the time-varying mmWave channel, and an off-grid model is incorporated for an accurate AoA acquisition. Essentially, the beamspace channel tracking is a quantized sparse Bayesian learning problem, which is solved under the expectation maximization (EM) framework. We employ the variational inference to calculate the statistics in the expectation step. In this way, we propose a variational inference joint channel tracking and data detection (VIJ-CTDD) algorithm, in which the detected data symbols are reused to enhance the tracking without extra pilot overhead. Finally, extensive simulations validate the superiority of the proposed VIJ-CTDD algorithms over several existing works.