A Robust Maximum Likelihood Channel Estimator for OFDM Systems

Application of existing maximum likelihood channel estimation (MLE) in orthogonal frequency division multiplexing (OFDM) systems requires knowledge of the effective length of channel impulse response (ELCIR) for achieving optimum performance. The analysis shows that the mean-squared error (MSE) is linearly related to ELCIR. Tracking the variation in ELCIR is thus very important for conventional MLE. But, incorporating a run-time update of ELCIR into the ML estimator turns out to be computationally expensive. Therefore, a modified ML channel estimator, which combines the ML estimation with a frequency-domain smoothing technique, is proposed. The proposed method introduces no extra complexity, and its performance has been proved using theoretical analysis and simulations to be robust to variation in ELCIR. Numerical results are provided to show the effectiveness of the proposed estimator under time-invariant and time-variant channel conditions.