Biased Zero-Error Probability for Adaptive Systems under Non-Gaussian Noise

초록

The criterion of zero-error probability provides a limitation on error probability functions being used for adaptive systems when the error samples are shifted by the influence of DC-bias noise. In this paper, we employ a bias term in the error distribution and propose a new criterion of the biased zero-error probability with error being zero. Also, by maximizing the proposed criterion on expanded filter structures, a supervised adaptive algorithm has been derived. From the simulation results of supervised equalization, the algorithm based on the proposed criterion yielded zero-centered and highly concentrated error samples without disturbance in the environments of strong impulsive and DC-bias noise.

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