抑制脉冲型噪声的高斯拖尾非线性函数设计

低频通信中脉冲型噪声会严重降低通信性能.针对脉冲型噪声的抑制问题，本文提出高斯拖尾零记忆非线性（Gaussian-tailed Zero Memory Nonlinearity，GZMNL）函数的最优化设计方法.GZMNL函数含有两个参数，分别控制其线性范围和拖尾程度，故适用于多种噪声分布.本文提出GZMNL设计以效能最大化为优化目标，采用自适应搜索算法来寻找GZMNL参数的最佳值.然后讨论了GZMNL在SαS（Symmetric α-Stable，SαS）噪声分布下的快速设计方法，以及在未知噪声分布时的稳健设计方法.最后，仿真SαS噪声和实测大气噪声数据的处理结果表明：本文设计方法在检测性能上能够接近最优非线性，且能够有效抑制未知分布的噪声.

Optimal Design of the Gaussian-Tailed Zero Memory Nonlinearity Function for Impulsive Noise Suppression

Impulsive noise can greatly degrade the performance of long wave communications.This paper proposes the optimal design of the Gaussian-tailed zero memory nonlinearity (GZMNL) function to suppress impulsive noise.The GZMNL function which was proposed for the symmetric α-stable (SαS) noise is not robust in applications,because of the lack of adaptive parameters.This paper proposes to design the GZMNL parameters adaptively to control the linear range and the tails,so that the GZMNL can be effective for various noise distributions.In the GZMNL design,the efficiency is employed as the objective function which is maximized over the GZMNL parameters.To solve this optimization problem,we develop a derivative-free optimization algorithm which searches the maximum efficacy adaptively.Considering practical applications,we propose two fast algorithms for the GZMNL design in the SαS noise,as well as a robust method for the GZMNL design in unknown noise distributions.Simulation results based on the SαS noise and real atmospheric noise show that the GZMNL design achieves almost the best nonlinearity in known noise distributions.The GZMNL design is effective and robust for unknown noise distributions.