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Nonlinear Channel Estimation Based on Particle Filtering for MIMO-OFDM Systems

Since accurate channel state information is crucial for coherent detection in a wireless MIMO-OFDM system, the performance of the MIMO-OFDM system critically depends on the availability of accurate channel estimation. This paper presents a channel estimation method base on particle filtering for wireless MIMO-OFDM systems. Based on the concept of sequential importance sampling and the use of Bayesian theory, particle filtering is particularly useful in dealing with nonlinear and non-Gaussian problems such as wireless channel estimation. Although this method presupposes no prior knowledge of channel statistics, it can track time-varying channel parameters instantaneously with the help of pilot symbols and particle filters. Computer simulations confirm that this method base on particle filtering has better performance than the other methods which are based on extended Kalman filtering (EKF) and LMS filtering