Novel Implementation of LMS Adaptive Algorithm for High Speed and Low Complexity

This paper presents the least-mean-square (LMS) adaptive filter for deriving its Architectures for high-speed and low complexity implementation. It is shown that the direct-form LMS adaptive filter has nearly the same critical path as its transpose-form counterpart, but provides much faster convergence and lower register complexity. From the critical-path evaluation, it is further shown that no pipelining is required for implementing a direct-form LMS adaptive filter for most practical cases, and can be realized with a very small adaptation delay in cases where a very high sampling rate is required. Based on these findings, this paper proposes three structures of the LMS adaptive filter: (i) Design 1 having no adaptation delays, (ii)Design 2 with only one adaptation delay, and(iii) Design 3 with two adaptation delays. Design1 involves the minimum area and the minimum energy per sample (EPS).We present here the optimization of design to reduce the number of pipeline delays along with the area, sampling period, and energy consumption. The proposed design is found to be more efficient in terms of the power-delay product (PDP) and energy-delay product (EDP) compared to the existing structures.