A Particle Filter for Frequency Synchronization in MIMO-OFDM Systems

A particle filter algorithm is investigated to perform joint estimation of carrier frequency offset (CFO) and channel in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) wireless communication systems. The filter marginalizes out the channel parameters from the sampling space in sequential importance sampling, and propagates them with the Kalman filter. Then the importance weights of CFO particles are evaluated according to the imaginary part of error between measurement and estimate. Moreover, the varieties of particles are maintained by sequential importance resampling. Simulations demonstrate the algorithm can estimate the CFO and channel with high accuracy, as well as has some robustness to the channel model variation.