A Comparison of Ungerboeck and Forney Models for Reduced-Complexity ISI Equalization

Abstract

This paper investigates the performance of reduced- state trellis-based ISI equalizers, which are based on the so- called Ungerboeck and Forney observation models. Although the two models are equivalent when the Viterbi or BCJR equalizer is employed, their performances differ significantly when using reduced-complexity methods. It is demonstrated that practical equalizers operating on the Forney model outperform those operating on the Ungerboeck model for high signal-to-noise ratios (SNRs), while the situation is reversed for low SNR levels. A novel theoretical reduced-complexity equalization strategy that improves on previous Ungerboeck-based equalizers is proposed.