Mean-Square Error Analysis of a Non-Uniform Subband Adaptive Filtering Structure with Critical Sampling

Adaptive subband structures have been proposed with the objective of increasing the convergence speed and/or reducing the computational complexity of conventional adaptive algorithms, mainly for applications which require a large number of adaptive coefficients. In this paper, we present a non-uniform subband structure with critical sampling, which is able of modeling an arbitrary FIR system with reduced aliasing. An LMS-type adaptation algorithm with normalized step-sizes, which works at the lowest downsampling rate and minimizes the average of the subband squared-errors, is derived for the proposed structure. A convergence analysis of the adaptation algorithm is presented, from which the steady-state mean-square error can be estimated.