A novel variable step size adjustment method based on channel output autocorrelation for the LMS training algorithm

摘要

The least mean squares (LMS) algorithm, the most commonly used channel estimation and equalization technique, converges very slowly. The convergence rate of the LMS algorithm is quite sensitive to the adjustment of the step-size parameter used in the update equation. Therefore, many studies have concentrated on adjusting the step-size parameter in order to improve the training speed and accuracy of the LMS algorithm. A novel approach in adjusting the step size of the LMS algorithm using the channel output autocorrelation (COA) has been proposed for application to unknown channel estimation or equalization in low-SNR in this paper. Computer simulations have been performed to illustrate the performance of the proposed method in frequency selective Rayleigh fading channels. The obtained simulation results using HIPERLAN/1 standard have demonstrated that the proposed variable step size LMS (VSS-LMS) algorithm has considerably better performance than conventional LMS, recursive least squares (RLS), normalized LMS (N-LMS) and the other VSS-LMS algorithms.