Abstract
The paper proposes a modification of the LMS algorithm that reduces the effect of finite wordlength on the adaptive filter performance. The proposed algorithm is based on repeatedly freezing the filter coefficients for a certain period of time and then updating them on the base of the average innovation term during the freezing period. Expressions are derived for the steady state mean square error and the convergence time of the proposed algorithm. It is found that the proposed algorithm poses a significantly higher resistance to roundoff errors than that of the conventional LMS algorithm. The derived expressions are found to be in an excellent agreement with computer simulation results.
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