An adaptive algorithm for stable decision-feedback filtering

A method of decision-feedback filtering that is particularly well suited for data that have undergone spectral nulling is introduced. The lack of spectral content causes a large eigenvalue spread of the data correlation matrix, with deleterious effects on the performance of conventional adaptive algorithms. An adaptive decision-feedback equalizer update algorithm that does not have these undesirable properties is presented. The algorithm calculates an iterative regularized inverse solution for the decision-feedback equalizer coefficients and applies appropriate attenuation (regularization) at frequencies lacking signal energy. This automatic estimation of the significant signal components provides an improvement over other methods that use matrix arithmetic to perform the same task. The other features of the algorithm are linear computational complexity and fast tracking capability. Simulation results that compare the algorithm to the conventional LMS and RLS algorithms are presented