Performance of multi-split decision feedback equalizer

In this paper, the idea of multi-split adaptive filtering is applied on DFE. The feed-forward and feedback sections in the DFE are divided into parallel sub-filters by imposing separately the symmetry and antisymmetry conditions on the impulse responses of filters by using appropriate and distinct sets of linear constraints. The purpose of this is to obtain a new structure that gives better performance than the conventional structure. The performance of multi-split DFE is tested with different algorithms (LMS, RLS, DCT-LMS) and compared to that of multi-split linear transversal Equalizer. convergence and complexity of the equalizer are two conflicting parameters. In order to improve the convergence rate with a small increase in complexity, the split adaptive filter is a good choice [4-6]. Split processing technique guarantees stability and give quicker convergence at the expense of a moderate increase in computation. In adaptive equalization, the convergence behavior is governed by the eigenvalue spread; a lower spread increases the convergence rate. It has been shown that whitening the equalizer input using some transformations such as Karhumen-Loeve transform and discrete cosine transform can significantly reduce the eigenvalue spread [4].