A minimum mean squared error (MMSE) equalizer is a way to effectively increase transmission performance for nonlinear Fourier transform (NFT) based communication systems. Other equalization schemes, based on nonlinear equalizer approaches or neural networks, are interesting for NFT transmission due to their ability to deal with nonlinear correlations of the NFTs’ eigenvalues and their coefficients. We experimentally investigated single- and dual-polarization long haul transmission with several modulation schemes and compared different equalization techniques including joint detection equalization and the use of neural networks. We observed that joint detection equalization provides range increases for shorter transmission distances while having low numeric complexity. We could further achieve bit error rates (BER) under HD-FEC for significant longer transmission distances in comparison to no equalization with different equalizers.

中文翻译：

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基于特征值通信的光传输信号处理技术

最小均方误差 (MMSE) 均衡器是一种有效提高基于非线性傅立叶变换 (NFT) 的通信系统传输性能的方法。其他基于非线性均衡器方法或神经网络的均衡方案对 NFT 传输很有趣，因为它们能够处理 NFT 特征值及其系数的非线性相关性。我们通过实验研究了具有多种调制方案的单极化和双极化长途传输，并比较了不同的均衡技术，包括联合检测均衡和神经网络的使用。我们观察到联合检测均衡为较短的传输距离提供了范围增加，同时具有较低的数字复杂度。