Maximum Likelihood Slow Frequency-Selective Fading Channel Estimation Using Frequency Domain Approach

Abstract

This paper addresses the channel estimation problem for slow frequency-selective fading channel using training sequence and maximum likelihood (ML) approach. Traditional works assumed symbol period spaced delay-tapped line model and additive white Gaussian noise (AWGN). Because of pre-filtering in the receiver front end, if the sampling rate is larger than one sample per symbol or sampling epoch is unknown (i.e., timing information is not available), AWGN model is not valid anymore. A more general ML channel estimation method using discrete Fourier transform (DFT) is derived with the assumption of colored Gaussian noise and over sampling. Similar idea can be adopted to derive the ML joint timing and phase estimation algorithm.Globecom 2000