A time-varying normalized mixed-norm LMS-LMF algorithm

The normalized least mean square (NLMS) algorithm is known to result in a faster convergence than the least mean square (LMS) algorithm but at the expense of a larger steady-state error. A time-varying normalized mixed-norm LMS-least mean fourth (LMF) algorithm is presented in this work to preserve the fast convergence of the NLMS algorithm while resulting in a lower steady-state error. The simulation results show that a substantial improvement, in both convergence time and steady state error, can be obtained with this mixed-norm algorithm.