An Iterative Channel Estimator for Indoor Wireless OFDM Systems

Abstract

Maximum-likelihood (ML) channel estimators (MLE) used in orthogonal frequency division multiplexing (OFDM) systems are known to be of low complexity, yet with performance comparable to that of minimum mean-squared error (MMSE) estimators. Our analysis shows that the mean-squared error (MSE) of a ML estimator is linearly related to the effective length of channel impulse response, M. Tracking the variation of M is thus very important for conventional MLE. But, incorporating a run-time update of M into the ML estimator turns out to be computationally expensive. In this paper, we propose a novel channel estimation scheme which is less M-dependent. This scheme combines ML estimation and frequency-domain smoothing systematically based on a simple iterative structure. The proposed iterative estimator has shown to be robust to channel variations and has implementation complexity similar to that of conventional MLE. Numerical results are provided to show the effectiveness of the proposed estimator under time-invariant and time-variant channel conditions