Bayesian Bilinear Inference for Joint Channel Tracking and Data Detection in Millimeter-Wave MIMO Systems

We propose a novel joint channel tracking and data detection (JCTDD) scheme to combat the channel aging phenomenon typical of millimeter-wave (mmWave) multiple-input multiple-output (MIMO) communication systems in high-mobility scenarios. The contribution aims to significantly reduce the communication overhead required to estimate time-varying mmWave channels by leveraging a Bayesian message passing framework based on Gaussian approximation, to jointly perform channel tracking (CT) and data detection (DD). The proposed method can be interpreted as an extension of the Kalman filter-based two-stage tracking mechanism to a Bayesian bilinear inference (BBI)-based joint channel and data estimation (JCDE) framework, featuring the ability to predict future channel state information (CSI) from both reference and payload signals by using an auto-regressive (AR) model describing the time variability of mmWave channel as a state transition model in a bilinear inference algorithm. The resulting JCTDD scheme allows us to track the symbol-by-symbol time variation of channels without embedding additional pilots, leaving any added redundancy to be exploited for channel coding, dramatically improving system performance. The efficacy of the proposed method is confirmed by computer simulations, which show that the proposed method not only significantly outperforms the state-of-the-art (SotA) but also approaches the performance of an idealized Genie-aided scheme.