Channel estimation and equalization in fading

This paper addresses the problem of channel tracking and equalization in frequency-selective fading channels. Low-order autoregressive models approximate the channel taps, where each tap is a circular complex Gaussian random process with the typical U-shaped spectrum, and uncorrelated with each other. A Kalman filter tracks the time-varying channel, using the decisions of an adaptive minimum-mean-squared-error decision-feedback equalizer (DFE). The DFE is optimized for decision delay /spl Delta/>0, which exhibits performance advantages over decision delay /spl Delta/=0 for a wide range of channels. The DFE staggered decisions cause the Kalman filter to also track the channel with a delay. The receiver also uses a channel prediction module to bridge the time gap between the Kalman channel estimation and the channel estimates needed for the DFE adaptation. The proposed algorithm offers good tracking behavior thus allowing for reduction in the amount of training symbols needed to effectively track a time-varying frequency selective channel.