Performance analysis of least mean square algorithm for different step size parameters with different filter order and iterations

This paper presents the performance analysis of Least Mean Square (LMS) algorithm for adaptive noise cancellation by varying its step size parameter μ for different filter order and no of iteration. The presented work has been simulated in MATLAB and verified that the step size parameter plays a vital role for implementation of Least Mean Square (LMS) algorithm. Increasing the step size parameter μ leads to fast convergence rate and instability of the least mean square algorithm. On the other side if the step size parameter μ is small then the error reduced to great amount but algorithm converges slowly and becomes stable. On the basis of obtained results we can conclude that step size parameter μ is directly proportional to convergence rate and error reduction and inversely proportional to stability. The work presented here also shown the comparison of actual weights and the estimated weights.