A Variable Step Size Adaptive Algorithm With Simple Parameter Selection - Tiglea, Daniel G.; Candido, Renato; Silva, Magno T. M. (IEEE signal processing letters, 2022)
We propose a normalized least mean squares algorithm with variable step size. Unlike other solutions, it has low computational cost, only three parameters that are simple to choose, and its steady-state performance can be easily predicted. Simulations show a competitive performance in comparison with other solutions, and validate our theoretical analysis.