A New Leaky-LMS Algorithm with Analysis
Tajuddeen
Gwadabe and Mohammad Salman
Department
of Electrical and Electronics Engineering, Mevlana University, Turkey
Abstract: Though the Leaky Least-Mean-Square (LLMS) algorithm mitigates the
drifting problem of the LMS algorithm, its performance is similar to that of
the LMS algorithm in terms of convergence rate. In this paper, we propose a new
LLMS algorithm that has a better performance than the LLMS algorithm in terms
of the convergence rate and at the same time solves the drifting problem in the
LMS algorithm. This better performance is achieved by expressing the cost
function in terms of a sum of exponentials at a negligible increase in the
computational complexity. The convergence analysis of the proposed algorithm is
presented. Also, a normalized version of the proposed algorithm is presented. The performance of the proposed algorithm is
compared to those of the conventional LLMS algorithm and a Modified version of
the Leaky Least-Mean-Square (MLLMS) algorithm in channel estimation and channel
equalization settings in additive white Gaussian and white and correlated
impulsive noise environments.
Keywords:
LLMS algorithm, channel estimation, channel equalization, impulsive noise.
Received September 24, 2014; accepted
June 2, 2015