A soft-input adaptive equalizer algorithm

Equalization of digital communication signals in a modern setting often has available probability distributions as desired inputs, rather than simply the desired symbols. Such data may be available, for example, in a turbo equalization setting. This paper presents an adaptive equalizer which takes probability distributions as inputs and trains its output to match the desired distributions, where the output distribution is obtained as a posterior error calculation based on the FIR filter output. Two training criteria are examined: Euclidean mean squared error between the output distribution and the desired distribution, and the relative entropy between these distributions are presented. Both LMS and RLS adaptation methods are developed.