Steady-state mean-square analysis of the deficient-length NLMS algorithm with reusing coefficient vector for white input

In many practical situations, the length of the system to be identified is extremely large and unknown so that the adaptive filter usually works in an under-modeling scenario, i.e., the length of the adaptive filter is less than that of the unknown system. Under this realistic case, we analyze the steady-state mean-square-deviation performance of the normalized least mean square with reusing coefficient-vector (NLMS-RC) algorithm for white input. Simulation results support the accuracy of our theoretical results.

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