Performance analysis of frequency-domain block LMS adaptive digital filters

The performance of the frequency-domain block least-mean-square (FBLMS) adaptive digital filters, whose filter weights are updated efficiently using the fast Fourier transform, is investigated. In particular, the convergence of the unconstrained FBLMS algorithm with reduced complexity, which is obtained by removing the constraint that has been known to be required in adjusting the frequency-domain weights based on overlap-save sectioning, is analyzed. The performance of the self-orthogonalizing FBLMS algorithm with improved convergence speed, in which different convergence factors normalized by frequency-domain power estimates are used for different frequency components of the weights, is also studied