Quantizing Input of LMS Algorithm Applied to Noisy Signal Prediction

摘要

In this paper a novel variation to the LMS algorithm is presented. In the Quantized Input LMS algorithm (QI-RLS), the input signal is quantized using a new function. The convergence and tracking power of the QI-LMS algorithm to the optimum Wiener weights is proved. The computational complexity and signal estimation error is lower than that of the standard LMS algorithm. The QI-LMS algorithm is used in the concealment of speech packet loss and prediction of noisy speech signals in wireless Wimax networks. Simulation results shows that this algorithm yields considerable error reduction and less computation time in comparison to the conventional LMS algorithm.