New Bayesian Learning Tracks Wireless Channel and Hardware Impairments.

This research presents a novel approach to improve the performance of massive multiple-input multiple-output (MIMO) wireless communication systems by addressing the complex issue of hardware impairments. These impairments, which include inter-symbol memory and inter-element coupling, significantly degrade the accuracy of channel estimation. The proposed framework, called MP-TTBDL, utilizes a residual recurrent gated unit (RGRU) to model the internal memory effects of hardware. A key innovation is the two-timescale Bayesian deep learning methodology. It recognizes that wireless channels change rapidly, while hardware impairments evolve much slower due to aging and environmental factors. By assigning distinct Markov priors for these different timescales, the system can more accurately track both phenomena. The framework employs a multi-slot factor graph and a message-passing algorithm, integrating turbo orthogonal approximate message passing for channel estimation and a deep approximate message passing procedure for impairment calibration, iteratively refining estimates. Simulation results indicate that the proposed framework robustly achieves lower channel estimation error compared to conventional compensators followed by channel estimation across various online impairment scenarios and signal-to-noise ratio conditions.