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Numerous variable step-size normalized least mean-square (VSS-NLMS) algorithms have been derived to solve the dilemma of fast convergence rate or low excess mean-square error in the past two decades. This paper proposes a new, easy to implement, nonparametric VSS-NLMS algorithm that employs the mean-square error and the estimated system noise power to(More)
Least-mean-square (LMS) and block LMS (BLMS) adaptive filters are generally believed to have similar step-size bounds for convergence. Similarly, convergence analyses of frequency-domain block LMS (FBLMS) adaptive filters have suggested that they have very restrictive convergence bounds. In this letter, we revisit Feuer's work and reveal a much larger(More)
Frequency domain adaptive filters have gamed much attention recently. Although some work on performance analysis has been reported, there is still much to be done. This paper presents a convergence analysis of the multidelay tiequency domain adaptive filter. We show, for the first time, the relationship between the convergence step-size and the convergence(More)