Leakage Estimation in DFT-based Channel Estimator for OFDM by using Minimum Mean Square Estimator

For high data rate communication with the required Quality of Service (QoS) in 3G and 4G systems, Orthogonal Frequency Division Multiplexing (OFDM) is proposed. We present a novel discrete Fourier transform (DFT)-based channel estimator for orthogonal frequency-division multiplexing (OFDM) systems. The conventional DFT-based estimator zeroes out noise-dominant values in the transform domain, which suffers from a poor performance in the presence of channel leakage. Some improved estimators have been proposed, but these estimators all focus on reducing the leaked energy. We propose to estimate the channel leakage by using the partial minimum mean square error (MMSE) filtering. When the channel delay is sufficiently less than the duration of an OFDM symbol, the partial MMSE filtering can be simplified. We propose several improved adaptive sparse channel estimation methods using Lp-norm normalized LMS (LP-NLMS) and Lp-norm normalized LMS (L0-NLMS). Simulation results show that the proposed channel estimator achieves much better performance than conventional estimators and well approaches the MMSE estimator. Comparing with previous methods, effectiveness of the proposed methods is confirmed by computer simulations.