Optimizing modulation and detection strategies for a given channel is critical to maximizing the throughput of a communication system. Such an optimization can be easily carried out analytically for channels that admit closed-form analytical models. However, this task becomes extremely challenging for nonlinear dispersive channels such as the optical fiber. End-to-end optimization through autoencoders (AEs) can be applied to define symbol-to-waveform (modulation) and waveform-to-symbol (detection) mappings, but so far it has been mainly shown for systems relying on approximate channel models. Here, for the first time, we propose an AE scheme applied to the full optical channel described by the nonlinear Schrödinger equation (NLSE). Transmitter and receiver are jointly optimized through the split-step Fourier method (SSFM) which accurately models an optical fiber. In this first numerical analysis, the detection is performed by a neural network (NN), whereas the symbol-to-waveform mapping is aided by the nonlinear Fourier transform (NFT) theory in order to simplify and guide the optimization on the modulation side. This proof-of-concept AE scheme is thus benchmarked against a manually-optimized NFT-based system and a three-fold increase in achievable distance (from 2000 to 6640 km) is demonstrated.

中文翻译：

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基于非线性傅里叶变换理论的分步傅里叶方法对相干光通信的端到端优化

优化给定信道的调制和检测策略对于最大化通信系统的吞吐量至关重要。对于允许封闭形式分析模型的通道，可以很容易地分析地执行这种优化。然而，这项任务对于非线性色散通道（如光纤）变得极具挑战性。通过自动编码器 (AE) 进行的端到端优化可用于定义符号到波形（调制）和波形到符号（检测）的映射，但到目前为止，它主要用于依赖近似信道模型的系统. 在这里，我们首次提出了一种应用于非线性薛定谔方程 (NLSE) 描述的全光通道的 AE 方案。发射器和接收器通过分步傅立叶方法 (SSFM) 联合优化，该方法可准确模拟光纤。在第一次数值分析中，检测由神经网络 (NN) 执行，而符号到波形的映射由非线性傅立叶变换 (NFT) 理论辅助，以简化和指导调制侧的优化。因此，这种概念验证 AE 方案以手动优化的基于 NFT 的系统为基准，并证明了可实现的距离（从 2000 到 6640 公里）增加了三倍。