AN IMPROVED FIXED-POINT LMS & RLS ADAPTIVE FILTER WITH LOW ADAPTATION-DELAY

An improved architecture for the implementation of a delayed least mean square adaptive filter is proposed in this paper. THE LEAST MEAN SQUARE (LMS) adaptive filter is the most popular and most widely used adaptive filter, not only because of its simplicity but also because of its satisfactory convergence performance. But conventional LMS adaptive filter involves a long critical path due to an inner-product computation to obtain the filter output. That critical path is required to be reduced by pipelined implementation called delayed LMS (DLMS) adaptive filter. The conventional delayed LMS adaptive filter architecture occupies more area, more power wastage and less performance then compare with this proposed architecture. The proposed LMS design offers less area-delay product (ADP) and energydelay product (EDP). Moreover, the proposed adaptive filter design is extended by replacing LMS algorithm to RLS (Recursive least squares) algorithm which leads to better performance, and also by adding bit-level pruning of the proposed architecture, which improves ADP and EDP further.