Subband blind equalization using wavelet filter banks

We propose an approach to decrease the computational cost and improve the convergence rate of blind equalizers using wavelet filter banks. Subband adaptive filtering is known for its improved convergence rate over the conventional least mean square (LMS) algorithm, even with lowering the computational cost; on the other hand, blind equalizers in many applications, such as mobile communication channels and digital subscriber lines, suffer from a poor convergence rate. We propose a subband equalization method using wavelet filter banks to improve the convergence rate and decrease the computational complexity of such equalizers and discuss its advantage over other subband adaptive filtering (SAF) schemes. This discussion is done on an important category of blind equalizers, a cyclostationarity based approach that is used in the case of blind fractionally spaced equalization (FSE) of channels.