Nonuniform Subband Adaptive Filtering 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 that require a large number of adaptive coefficients. In this paper, we present a nonuniform subband structure with critical sampling, which is capable of modeling an arbitrary finite-impulse response (FIR) system with reduced aliasing. A least-mean-square (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 its convergence rate and steady-state mean-square error can be estimated.