Analysis of Variable Step-size Order Statistic LMS-based Algorithms with a Data-reuse Factor

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Suchada Sitjongsataporn
Kodchakorn Na Nakornphanom


In this paper, we present variable step-size algorithms based on an order statistic normalized averaged least mean square (OS-NALMS) with a data-reuse factor. An optimal step-size parameter is investigated by means of mean square deviation criterion, we then modify in the recursion form. Two of new variable step-size approaches are introduced with regard to the optimal step-size algorithm. In order to reduce the complexity, a variable data-reuse factor approach is applied to the number of reused gradient based estimate sequences for averaged least mean square algorithm. Simulation results demonstrate that the performance of proposed variable step-size OS-NALMS algorithms can obtain in terms of fast convergence and robustness.

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