Gray encoded fixed-point LMS adaptive filter architecture for the harmonics power line interference cancelling

Abstract

This paper proposes the implementation of dedicated hardware architecture for the Least Mean Square (LMS) adaptive filtering algorithm by using Gray encoding arithmetic operators, whose main goal is to cancel the interferences in the signal of interest. In the used scheme, from a 60Hz reference signal, the algorithm is able to estimate the superior harmonics, using after these results for the cancelling of interferences related to the signal of interest. One of most widely used technique for the switching activity reduction uses signal encoding. In this work, the proposed adaptive filtering architecture uses a Hybrid encoding in its data buses, which is a compromise between the minimal input dependency presented by the Binary encoding and the low switching characteristic of the Gray encoding. The main results showed that the Hybrid multipliers are more efficient than the Binary ones, by presenting less power consumption in some cases. Moreover, the implemented adaptive filtering architectures were validated and compared in both Binary and Hybrid encoding. The efficiency of the implemented Hybrid filter for the cancelling of interferences was proved by reducing more power than the Binary one. By the results, we conclude that it could be practicable to implement an adaptive filtering architecture operating on Hybrid encoding.