Self-Recovering Equalization and Carrier Tracking in Two-Dimensional Data Communication Systems

TLDR

Conventional equalization and carrier recovery algorithms for minimizing mean‑square error in digital communication systems generally require an initial training period during which a known data sequence is transmitted and properly synchronized at the receiver. This paper solves the general problem of adaptive channel equalization without resorting to a known training sequence or to conditions of limited distortion. The equalizer adapts by minimizing a new class of nonconvex cost functions that characterize intersymbol interference independently of carrier phase and data symbol constellation, thereby avoiding the need for a training sequence. The self‑recovering algorithms converge without carrier recovery, enabling decision‑directed carrier phase tracking, and their convergence properties were mathematically analyzed and confirmed by simulation.

Abstract

Conventional equalization and carrier recovery algorithms for minimizing mean-square error in digital communication systems generally require an initial training period during which a known data sequence is transmitted and properly synchronized at the receiver. This paper solves the general problem of adaptive channel equalization without resorting to a known training sequence or to conditions of limited distortion. The criterion for equalizer adaptation is the minimization of a new class of nonconvex cost functions which are shown to characterize intersymbol interference independently of carrier phase and of the data symbol constellation used in the transmission system. Equalizer convergence does not require carrier recovery, so that carrier phase tracking can be carried out at the equalizer output in a decision-directed mode. The convergence properties of the self-recovering algorithms are analyzed mathematically and confirmed by computer simulation.