Efficient nonlinearity compensation in fiber-optic communication systems is considered a key element to go beyond the “capacity crunch”. One guiding principle for previous work on the design of practical nonlinearity compensation schemes is that fewer steps lead to better systems. In this paper, we challenge this assumption and show how to carefully design multi-step approaches that provide better performance–complexity trade-offs than their few-step counterparts. We consider the recently proposed learned digital backpropagation (LDBP) approach, where the linear steps in the split-step method are re-interpreted as general linear functions, similar to the weight matrices in a deep neural network. Our main contribution lies in an experimental demonstration of this approach for a 25 Gbaud single-channel optical transmission system. It is shown how LDBP can be integrated into a coherent receiver DSP chain and successfully trained in the presence of various hardware impairments. Our results show that LDBP with limited complexity can achieve better performance than standard DBP by using very short, but jointly optimized, finite-impulse response filters in each step. This paper also provides an overview of recently proposed extensions of LDBP and we comment on potentially interesting avenues for future work.

中文翻译：

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用机器学习重新审视高效的多步非线性补偿：实验演示

光纤通信系统中的高效非线性补偿被认为是超越“容量紧缩”的关键因素。以前关于实际非线性补偿方案设计工作的一个指导原则是，更少的步骤导致更好的系统。在本文中，我们挑战了这一假设，并展示了如何精心设计多步方法，以提供比其少步方法更好的性能-复杂性权衡。我们考虑最近提出的学习数字反向传播 (LDBP) 方法，其中拆分步法中的线性步长被重新解释为一般线性函数，类似于深度神经网络中的权重矩阵。我们的主要贡献在于对 25 Gbaud 单通道光传输系统的这种方法的实验演示。展示了如何将 LDBP 集成到相干接收器 DSP 链中，并在存在各种硬件损伤的情况下成功训练。我们的结果表明，通过在每个步骤中使用非常短但联合优化的有限脉冲响应滤波器，具有有限复杂性的 LDBP 可以获得比标准 DBP 更好的性能。本文还概述了最近提出的 LDBP 扩展，我们评论了未来工作的潜在有趣途径。