High sample rate architectures for block adaptive filters

In this paper we propose a variety of architectures for implementing block adaptive filters in the time-domain. These filters are based on a block implementation of the least mean squares (BLMS) algorithm. First, we present an architecture which directly maps the BLMS algorithm into an array of processors. Next, we describe an architecture where the weight vector is updated without explicitly computing the filter error. Third, we describe an architecture which exploits the redundant computations of overlapping windows. All the architectures have a significantly smaller sample period compared to frequency domain implementations. Moreover, the sample periods can be reduced even further by applying relaxed look-ahead techniques