Blind equalization with criterion with memory nonlinearity
Yuanjie Chen · Optical Engineering · 1992
Stop-and-go adaptation rules that are utilized to improve the blind convergence characteristics of the conventional and sign decision-directed algorithms are proposed and examined. They are based on the Sato- and Godard-type errors, which are utilized in many blind deconvolution applications. The convergence rates achieved by the algorithms with quadrature amplitude modulated signal constellations and nonminimum phase communication channels are compared. Based on a new criterion, the optimal values of the Sato and Godard error parameters are redefined. The optimality of the new parameter values is confirmed by means of computer simulations.