Joint CFO and time-varying channel estimation by particle filtering in OFDM systems

In this paper, we propose a new algorithm for orthogonal frequency-division-multiplexing (OFDM) systems. The algorithm is based on particle fitering (PF) for joint carrier recovery and channel estimation in slow Rayleigh fading channel. The algorithm has been designed to work with both the parametric L-path channel model (with known path delays) and the equivalent discrete-time channel model. PF is a powerful tool for estimating and tracking parameters in non-linear model. In the algorithm, we use a combination of Kalman filtering and Sequential Monte Carlo Sampling to estimate the channel fading coefficients and the posterior probability density of the unknown carrier frequency offset (CFO). Afterwards, the channel matrix is thus easily computed, and the data symbol is estimated with free intercarrier interference (ICI) by using MMSE equalizer. It is shown that our algorithm has a good performance in terms of MSE and BER and approaches the BER of the ideal case for which the channel response and CFO are known.