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Misalignment analysis and insights into the performance of clipped-input LMS with correlated Gaussian data

The three-level clipped input least-mean-square (CLMS) adaptive algorithm is known to have low complexity that is suitable for the identification of long finite impulse response of unknown systems. In this paper we analyze the performance of CLMS which allows one to gain insights into its convergence property and the amount of steady-state misalignment error for both time-invariant and time-varying systems perturbed by correlated Gaussian input. Arising from our analysis, we derive the optimal step-size for CLMS to achieve the minimum possible steady-state misalignment and compare its results with the performance of LMS adaptive algorithm. The accuracy of our derivations is evaluated with simulation results.