High-speed CORDIC based parallel weight extraction for QRD-RLS adaptive filtering

Jun Ma, Keshab K Parhi, Ed F. Deprettere

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In this paper, a very high-speed Cordic based parallel weight extraction algorithm for QRD-RLS adaptive filtering is developed using the matrix lookahead technique. The algorithm is based on inverse updating and is pipelined at fine-grain level. The proposed architecture can be operated at arbitrarily high sample rates, and consists of only Givens rotations which can be mapped onto a Jacobi specific dataflow processor. It requires a complexity of O(Mp2) Givens rotations per sample period, where p is the size of the input data samples, and M is the pipelining level.

Original languageEnglish (US)
JournalProceedings - IEEE International Symposium on Circuits and Systems
Volume5
StatePublished - Jan 1 1998

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Adaptive filtering

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abstract = "In this paper, a very high-speed Cordic based parallel weight extraction algorithm for QRD-RLS adaptive filtering is developed using the matrix lookahead technique. The algorithm is based on inverse updating and is pipelined at fine-grain level. The proposed architecture can be operated at arbitrarily high sample rates, and consists of only Givens rotations which can be mapped onto a Jacobi specific dataflow processor. It requires a complexity of O(Mp2) Givens rotations per sample period, where p is the size of the input data samples, and M is the pipelining level.",
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AB - In this paper, a very high-speed Cordic based parallel weight extraction algorithm for QRD-RLS adaptive filtering is developed using the matrix lookahead technique. The algorithm is based on inverse updating and is pipelined at fine-grain level. The proposed architecture can be operated at arbitrarily high sample rates, and consists of only Givens rotations which can be mapped onto a Jacobi specific dataflow processor. It requires a complexity of O(Mp2) Givens rotations per sample period, where p is the size of the input data samples, and M is the pipelining level.

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