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Proceedings Paper

Pipelined implementation of high-speed STAR-RLS adaptive filters
Author(s): Kalavai J. Raghunath; Keshab K. Parhi
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Paper Abstract

The popular QR decomposition based recursive least-squares (RLS) adaptive filtering algorithm (referred to as QRD-RLS) has a limited speed of operation depending on the processing time of each individual cell. A new scaled tangent rotation based STAR-RLS algorithm has been designed which is suitable for fine-grain pipelining and also has a lower complexity. The inter-cell communication is also reduced by about half. A direct application of look-ahead to STAR-RLS can still lead to some increase in hardware. In this paper look- ahead is applied using delayed update operations such that the complexity is reduced while maintaining a fast convergence. The pipelined STAR-RLS (or PSTAR-RLS) algorithm requires the same number of operations (multiplications or divisions) as the serial STAR-RLS algorithm. Practical issues related to the STAR-RLS algorithm such as numerical stability and dynamic range are also examined.

Paper Details

Date Published: 1 November 1993
PDF: 12 pages
Proc. SPIE 2027, Advanced Signal Processing Algorithms, Architectures, and Implementations IV, (1 November 1993); doi: 10.1117/12.160428
Show Author Affiliations
Kalavai J. Raghunath, Univ. of Minnesota/Twin Cities (United States)
Keshab K. Parhi, Univ. of Minnesota/Twin Cities (United States)

Published in SPIE Proceedings Vol. 2027:
Advanced Signal Processing Algorithms, Architectures, and Implementations IV
Franklin T. Luk, Editor(s)

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