Adaptive filtering of nonlinear systems with memory using quantized mean field annealing

A technique for adaptive filtering of nonlinear systems with memory has been developed which combines quantized mean field annealing (QMFA) and conventional recursive-least-squares/fast-transversal filter (RLS/FTF) adaptive filtering. Unlike other methods which rely on Volterra and Wiener models, this technique can efficiently handle large order nonlinearities with or without memory. The nonlinear channel is divided into a memory nonlinearity followed by a dispersive linear system. Assuming that the dispersive linear system is stationary during initialization and the nonlinearity does not change while the dispersive linear system varies with time, QMFA is applied to obtain the coefficients and the order of the memory of the nonlinearity and RLS/FTF is applied to determine the weights of the dispersive linear system. This method was applied to a full duplex digital subscriber loop. Simulations show that the performance of the technique is superior to that of ordinary RLS/FTF and steepest descent algorithms