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.
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