Nonlinear frequency division multiplexing (NFDM) communication systems that are based on the nonlinear Fourier transform (NFT), have seen a rapid improvement in performance and transmission reach over just a few years. However, such an improvement is now being slowed down by fundamental challenges such as fiber loss and noise. As the NFT theory is defined over a lossless transmission fiber, a strong research focus has been dedicated to either improve the lossless assumption for practical fibers, by adapting the theory to approximately account for the fiber loss, or by devising encoding schemes that increase the robustness of the NFT to the fiber attenuation. However, the proposed solutions provide only minimal benefits to the system performance, especially for long fiber spans as in deployed links. Alternatively, a detection strategy based on replacing a conventional NFT receiver with a time-domain Neural network (NN)-based symbol decisor has been numerically proposed. Here, we extend such an idea by validating it experimentally. In order to apply the method in an experimental environment, the impact of phase noise, and receiver frequency offset needs to be addressed. We, therefore, propose a novel time-domain receiver architecture that combines a two-stage iterative carrier recovery with a NN-based symbol decisor. The carrier recovery, itself based on a NN for phase estimation, is numerically, and experimentally characterized. The proposed receiver has been evaluated for single-polarization two-eigenvalue transmission at 1 GBd. A two-fold increase in the transmission reach is enabled by the NN receiver ($\approx$1600 km) compared to a conventional NFT receiver ($\approx$560 km) for a practical link using 80-km spaced erbium-doped fiber amplifier (EDFAs).

中文翻译：

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神经网络接收器非线性频分复用传输实验演示

基于非线性傅立叶变换 (NFT) 的非线性频分复用 (NFDM) 通信系统在短短几年内就在性能和传输范围方面取得了快速提升。然而，这种改进现在正因光纤损耗和噪声等基本挑战而放缓。由于 NFT 理论是在无损传输光纤上定义的，因此一个强有力的研究重点一直致力于改进实际光纤的无损假设，通过调整理论以近似解释光纤损耗，或者通过设计增加鲁棒性的编码方案NFT 对光纤衰减的影响。然而，所提出的解决方案对系统性能的好处微乎其微，尤其是对于部署链路中的长光纤跨距。或者，已经在数值上提出了一种基于用基于时域神经网络 (NN) 的符号决策器代替传统 NFT 接收器的检测策略。在这里，我们通过实验验证来扩展这样的想法。为了在实验环境中应用该方法，需要解决相位噪声和接收机频率偏移的影响。因此，我们提出了一种新颖的时域接收器架构，该架构将两阶段迭代载波恢复与基于 NN 的符号决策器相结合。载波恢复本身基于用于相位估计的 NN，在数值上和实验上都得到了表征。建议的接收器已针对 1 GBd 的单极化双特征值传输进行了评估。