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Robust Blind Learning Algorithm for Nonlinear Equalization Using Input Decision Information

机译：输入决策信息的非线性均衡鲁棒盲学习算法

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摘要

In this paper, we propose a new blind learning algorithm, namely, the Benveniste–Goursat input–output decision (BG-IOD), to enhance the convergence performance of neural network-based equalizers for nonlinear channel equalization. In contrast to conventional blind learning algorithms, where only the output of the equalizer is employed for updating system parameters, the BG-IOD exploits a new type of extra information, the input decision information obtained from the input of the equalizer, to mitigate the influence of the nonlinear equalizer structure on parameters learning, thereby leading to improved convergence performance. We prove that, with the input decision information, a desirable convergence capability that the output symbol error rate (SER) is always less than the input SER if the input SER is below a threshold, can be achieved. Then, the BG soft-switching technique is employed to combine the merits of both input and output decision information, where the former is used to guarantee SER convergence and the latter is to improve SER performance. Simulation results show that the proposed algorithm outperforms conventional blind learning algorithms, such as stochastic quadratic distance and dual mode constant modulus algorithm, in terms of both convergence performance and SER performance, for nonlinear equalization.
机译：在本文中，我们提出了一种新的盲学习算法，即Benveniste-Goursat输入-输出决策（BG-IOD），以增强基于神经网络的均衡器对非线性通道均衡的收敛性能。与仅使用均衡器的输出来更新系统参数的常规盲学习算法相反，BG-IOD利用一种新型的额外信息，即从均衡器的输入获得的输入决策信息，来减轻影响非线性均衡器结构对参数学习的影响，从而提高了收敛性能。我们证明，利用输入决策信息，可以实现理想的收敛能力，即如果输入SER低于阈值，则输出符号错误率（SER）始终小于输入SER。然后，采用BG软交换技术结合输入和输出决策信息的优点，其中前者用于保证SER收敛，而后者则用于提高SER性能。仿真结果表明，该算法在收敛性能和SER性能两方面都优于传统的盲二次学习算法，例如随机二次距离算法和双模恒模算法。

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