MCMC-based channel estimation for OFDM systems in dispersive time-varying channels

Channel estimation is therefore required as an integral part of the receiver design. The channel model adopted in this paper allows the CIR to vary within one OFDM symbol. To facilitate the channel estimation solution, maximum likelihood (ML) estimator adopts a second-order Taylor series expansion to approximate the intercarrier interference (ICI) caused by Doppler frequency shifts to simplify the channel estimation problem. However, this method which needs to find the roots for a third-order polynomial is so complex. In this paper, we investigate a Markov Chain Monte Carlo (MCMC)-based channel estimation for OFDM systems in dispersive time-varying channels. The proposed method is performing similar to ML algorithm with lesser computational complexity. Moreover, our proposed method can adopt a second/third or even higher-order Taylor series expansion to approximate the ICI, avoiding to find the roots for a high polynomial.