QRD - RLS Adaptive Filtering


The main limitation of FQRD-RLS algorithms is that they lack an explicit weight vector term. Furthermore, they do not directly provide the variables allowing for a straightforward computation of the weight vector as is the case with the conventional QRD-RLS algorithm, where a back-substitution procedure can be used to compute the coefficients. Therefore, the applications of the FQRD-RLS algorithms are limited to certain output-error based applications (e.g., noise cancelation), or to applications that can provide a decision-feedback estimate of the training signal (e.g., equalizers operating in decision-directed mode). This chapter presents some observations that allow us to apply the FQRD-RLS algorithms in applications that traditionally have required explicit knowledge of the transversal weights. Section 11.1 reviews the basic concepts of QRD-RLS and the particular FQRD-RLS algorithm that is used in the development of the new applications. Section 11.2 describes how to identify the implicit FQRD-RLS transversal weights. This allows us to use the FQRD-RLS algorithm in a system identification setup. Section 11.3 applies the FQRD-RLS algorithm to burst-trained systems, where the weight vector is updated during a training phase and then kept fixed and used for output filtering. Section 11.4 applies the FQRD-RLS algorithm for single-channel active noise control, where a copy of the adaptive filter is required for filtering a different input sequence than that of the adaptive filter. A discussion on multichannel and lattice extensions is provided in Section 11.5. Finally, conclusions are drawn in Section 11.6. Stefan Werner Helsinki University of Technology, Espoo – Finland e-mail: stefan.werner@tkk.fi Mohammed Mobien Helsinki University of Technology, Espoo – Finland e-mail: mobien@ieee.org

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@inproceedings{Apolinrio2008QRDR, title={QRD - RLS Adaptive Filtering}, author={Jos{\'e} Antonio Apolin{\'a}rio and John G. McWhirter}, year={2008} }