Phase Noise in MIMO Systems: Bayesian Cramer-Rao Bounds and Soft-Input Estimation
This paper addresses the problem of estimating time varying phase noise caused by imperfect oscillators in Multi-Input Multi-Output (MIMO) systems. The estimation problem is parameterized in detail and based on an equivalent signal model its dimensionality is reduced to minimize the overhead associated with phase noise estimation. New exact and closed-form expressions for the Bayesian Cramer-Rao Lower Bounds (BCRLBs) and soft-input Maximum A Posteriori (MAP) estimators for online, i.e., filtering, and offline, i.e., smoothing, estimation of phase noise over the length of a frame are derived. Simulations demonstrate that the proposed MAP estimators' Mean-Square Error (MSE) performances are very close to the derived BCRLBs at moderate-to-high signal-to-noise ratios.