On the Learning Behavior of Decision-Feedback Equalizer

The performance analysis of DFE equalizers, either fractional or symbol-spaced, is commonly based on the Wiener solution. This solution can only be computed once characteristic values of the channel and noise variance are known. In the "real" world, the solution needs to be estimated. In low-bit rate systems, complexity of algorithms is usually not an issue and Least-squares solutions with high accuracy are possible. For higher bit rate systems, however, a gradient-type procedure like the LMS algorithm seems unavoidable. Given a training sequence of limited length, the learning behavior of LMS can considerably worsen the performance of the DFE. This paper shows some insight of undesired effects.