Modified LMS Adaptive Algorithm for the determination of Maximum SNR using Self-Adaptation Technique of Noise Factor for Communication System

Abstract

The most often used adaptive filter (AF) is the least-mean-square (LMS) filter. This filter has many applications in the area of communication and signal processing. System identification represents a significant use case for adaptive filters. This paper represents a new structure/model of system identification and adaptive noise cancellation (ANC) using state-of-the-art LMS-AF. The paper expressed the algorithm of updated LMS-AF. The major parts of the proposed model are- two adaptive filters, one LMS filter, one correlation function, and one auto-correlation function. The research investigation for the proposed model is based on the self-adaptation or self-learning method to obtain the maximum signal to noise ratio (SNR) based on the real-time inputs. The proposed structure/model converges towards the ideal LMS-ANC system. This paper also includes mathematical analysis, simulation, results, and discussion. The most important comparison parameters are the SNR and mean-square-error (MSE).