Adaptive Equalization Via Fast Quantized-State Methods

Abstract

In adaptive equalization, there is a tradeoff between convergence rate of the equalizer tap coefficients, the computational speed for each adjustment, the implementation complexity, and algorithm robustness. Parameter update schemes called quantized state (QS) schemes, and fast versions of these schemes termed fast quantized state (FQS) schemes developed within a related context, are here applied to achieve attractive tradeoff options not previously available for adaptive equalization. Three novel simplifications to the QS schemes are introduced and justified by their performance characteristics in adaptive equalization. One simplification is to abandon the likeness to the method of instrumental variables (IV), where the "instrumental variable" is the quantized state vector, and introduce more quantization. Another simplification is to replace asymptotically Toeplitz matrices, or their inverses, by Toeplitz matrices to take advantage of fast schemes for updating QS schemes or taking their inverses.