Parallel implementation of a class of algorithms linking NLMS and block RLS
نویسنده
چکیده
In this paper, first a brief review is given of a fully pipelined algorithm for recursive least squares (RLS) estimation, based on socalled ‘inverse updating’. Then a specific class of (block) RLS algorithms is considered, which embraces normalized LMS as a special case (with block size equal to one). It is shown that such algorithms may be cast in the ‘inverse-updating RLS’ framework. This allows one to achieve any degree of pipelining, by performing algorithmic transformations which eliminate critical feedback loops in the original algorithms.
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