Implementation of Fixed-Point LMS Adaptive Filter for Area-Delay-Power Efficient Based on Low Adaptation-Delay

This paper presents the least-mean-square (LMS) adaptive filter for deriving its Architectures for high-speed and low complexity implementation. Among many adaptive algorithms that exist in the open literature, the class of approaches which are derived from the minimization of the mean squared error between the output of the adaptive filter and some desired signal seems to be the most popular. Probably the simplest algorithm belonging to this class is the Least Mean Squared (LMS) algorithm which has the advantage of low complexity and simplicity of implementation. One of the main concerns in all practical situations is to develop algorithms which provide fast convergence of the adaptive filter coefficients and in the same time good filtering performance. There are four main classes of applications where the adaptive filters were applied with success, namely: system identification, inverse modeling, prediction and interference canceling. An efficient architecture for the implementation of a delayed least mean square adaptive filter for achieving lower adaptation-delay and area-delay-power efficient implementation, proposed methodology is of a novel partial product generator and a strategy for optimized balanced pipelining across the time-consuming combinational blocks of the structure. The migration of DSP adaptive filter to RTL makes the algorithm much faster.