An adaptive channel equalizer based on sign permutation filtering framework

Channel equalization is fundamental to modern digital communication systems. It is used to deal with channel noise, channel distortion, multi-path problems, and multi-user interference. Examples can be found in consumer areas such as digital TV and personal communication systems, where various equalizers are used to increase the signal to noise ratio of an incoming signal and/or to reduce the bit error rate. In this paper, we propose a new class of digital equalizer based on a modular class of non-linear filters denoted as sign permutation filters. Sign permutation filters were originally based on the binary permutation filter theory studied by Kim et al (1994, 1995). While binary permutation filters are based on the space-rank ordering of the binary input samples, the sign permutation filters utilize the sign permutation information, or, the frequency of zero-crossings embedded in the underlying signals. By varying the degree of utilization of the sign permutation information utilized in the estimate, a variety of nonlinear equalizers is obtained. Simulations are presented to demonstrate the modular structure of the proposed class of nonlinear equalizers