Pilot Decontamination via Data-aided Channel Tracking in Cell-free Massive MIMO

Abstract

As cell-free massive MIMO emerges as a promising technology for future wireless networks, robust channel estimation becomes increasingly important. Channel estimation relies on pilot transmissions, and with increasing number of users, pilot reuse inevitably introduces contamination that degrades channel estimation and symbol detection. This limitation is compounded by rapidly evolving channels, which render the estimates obsolete over time. To address both the problems, viz. pilot contamination and channel aging without additional pilot overhead, we propose a Kalman filter-based data-aided channel tracking strategy. By leveraging the symbol estimates as virtual pilots, the proposed approach successively refines the channel estimates over time. Unlike the conventional Kalman filter model, here, the observation model depends on symbol estimates, introducing symbol uncertainty that gets coupled with channel uncertainty. This requires a careful calculation of the innovation covariance matrices. Additionally, we discuss a stochastic-gradient-descent based tracking approach with low computational complexity. Extensive simulations are used to demonstrate that successive refinement progressively decontaminates the channel estimate, achieving lower channel estimation error and improved spectral efficiency.