Equivalent output-filtering using fast QRD-RLS algorithm for burst-type training applications

Fast QR decomposition RLS (FQRD-RLS) algorithms are well known for their good numerical properties and low computational complexity. The FQRD-RLS algorithms do not provide access to the filter weights, and their uses have so far been limited to problems seeking an estimate of the output error signal. In this paper we present techniques which allow us to reproduce the equivalent output signal corresponding to any input-signal applied to the weight vector of the FQRD-RLS algorithm. As a consequence, we can extend the range of applications of the FQRD-RLS to include problems where the filter weights are periodically updated using training data, and then used for fixed filtering of a useful data sequence, e.g., burst-trained equalizers. The proposed output-filtering techniques are tested in an equalizer setup. The results verify our claims that the proposed techniques achieve the same performance as the inverse QRD-RLS algorithm at a much lower computational cost