The least-mean-squares (LMS) algorithm is the most popular algorithm inadaptive filtering. Several variable step-size strategies have been suggestedto improve the performance of the LMS algorithm. These strategies enhance theperformance of the algorithm but a major drawback is the complexity in thetheoretical analysis of the resultant algorithms. Researchers use severalassumptions to find closed-form analytical solutions. This work presents aunified approach for the analysis of variable step-size LMS algorithms. Theapproach is then applied to several variable step-size strategies andtheoretical and simulation results are compared.
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