Time-varying carrier offset tracking in OFDM systems using particle filtering

In this paper we address the problem of time varying carrier frequency offset (CFO) estimation for mobile OFDM systems. The offset is modeled using a nonlinear state space model. The estimation and tracking is based on a sequential Monte Carlo method called particle filter. Particle filtering is an attractive method because it suits well for nonlinear state-space models and works reliably even if the a posteriori distribution of the state is skewed, multimodal or heavy-tailed. Significant improvement in tracking performance over extended Kalman filtering (EKF) solution is shown in simulations.