Improved Channel Estimation Using Wavelet Denoising for OFDM and OFDMA Systems

Abstract

Least Square (LS) channel estimation has been widely used in OFDM (Orthogonal Frequency Division Multiplexing) and OFDMA (Orthogonal Frequency Division Multiplexing Access) systems. However, it's rather sensitive to Guassian white noise. In this paper, we present a new algorithm which deals with the LS estimation results through wavelet shrinkage denoising based on Stein’s unbiased risk estimation (SURE) criterion. This algorithm can effectively remove the influence of noise in the channels and minimize the estimation risk. Consequently, the sensitivity to noise of LS estimation is diminished. Simulation in the scenario of IEEE802.16 downlink transmission shows that the proposed algorithm has significant advantage over LS and modified LS estimators.