Factor-graph algorithms for equalization

In this paper, we use the factor-graph framework to describe the statistical relationships that arise in the equalization of data transmitted over an intersymbol interference channel, and use it to develop several new algorithms for linear and decision feedback approaches. Specifically, we examine both unconstrained and constrained linear equalization and decision feedback equalization of a sequence of nonidentically distributed symbols that are transmitted over a linear, possibly time-varying, finite-length channel and then corrupted by additive white noise. Factor graphs are used to derive algorithms for each of these equalization tasks, including fast implementations. One important application of these algorithms is linear turbo equalization, which requires a linear equalizer that can process observations of nonidentically distributed transmitted symbols. We show how the output of these factor-graph-based algorithms can be used in an efficient implementation of a linear turbo equalizer.