TREE-STRUCTURED PIECEWISE-LINEAR ADAPTIVE EQUALIZATION

We propose using a tree-structured piecewise linear filter as an adaptive equalizer. The tree equalizer is constructed as follows. Each node in a tree is associated with a linear filter restricted to a polygonal domain, and this is done in such a way that each subtree is associated with a piecewise linear filter. A training sequence is used to adaptively update the filter coefficients and domains at each node, and to select the best subtree and corresponding piecewise linear filter. The tree-structured approach offers several advantages. First, it makes use of standard linear adaptive filtering techniques at each node to find the corresponding conditional linear filter. Second, it allows for efficient selection of the subtree and corresponding piecewise linear filter of appropriate complexity. Overall, the approach is computationally efficient and conceptually simple. Numerical experiments are performed to show the advantages of tree-structured piecewise linear and piecewise decision feedback equalizers over linear, polynomial, and decision feedback equalizers for the equalization of channels with severe intersymbol interference.