Channel Tracking and AoA Acquisition in Quantized Millimeter Wave MIMO Systems

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 time-varying model to capture the sparsity and temporal correlation of the 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 first employ the Bussgang decomposition to linearize quantized signals, based on which, the Kalman filtering (KF) is adapted for the statistics' update in the expectation step. In this way, we propose a Bussgang joint channel tracking and data detection (BJ-CTDD) algorithm, in which the detected data symbols are reused to enhance the tracking without extra pilot overhead. To further reduce the computational burden caused by the KF, a variational inference joint channel tracking and data detection (VIJ-CTDD) algorithm is proposed. Finally, extensive simulations validate the superiority of the proposed BJ-CTDD and VIJ-CTDD algorithms over several existing works.