A weighted normalized frequency domain LMS adaptive algorithm

A general filtering scheme is presented for obtaining an input power estimate for setting the convergence parameter μ separately in each frequency bin of a frequency-domain LMS adaptive filter (FDAF) algorithm. A linear filtering operation is performed on the magnitude square of the input data and incorporated directly into the algorithm as a data-dependent time-varying stochastic μ(n). The mean performance of the weighted normalized frequency domain LMS algorithm (WNFDAF) is analyzed using independent and identically distributed Gaussian data, and the results are validated by the Monte Carlo simulations of the algorithm. The simulations are also used to study the weight transient behavior. The simulations suggest that sort smoothing times are sufficient for rapid weight convergence without large fluctuations in the power estimates significantly affecting transient weight behavior