Optimal and efficient designs of Gaussian-tailed non-linearity in symmetric α-stable noise

Optimal and efficient designs of Gaussian-tailed non-linearity in symmetric α-stable noise

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This Letter proposes two methods for the Gaussian-tailed zero-memory non-linearity (GZMNL) design in symmetric -stable noise. The optimal GZMNL is designed by maximising the efficacy via a derivative-free method. The efficient GZMNL is designed by polynomial fitting with the derived coefficients. Simulated results demonstrate that the GZMNL designs are nearly as optimal as the locally optimal detector, with an advantage of closed-form formulas.


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