Zhao Weiwei, Cheng Yijun, Xiang Meng, Tang Ming, Qin Yuwen, Fu Songnian
Opt Express. 2022 Oct 24;30(22):39725-39735. doi: 10.1364/OE.474956.
Fiber nonlinearity is one of the major impairments for long-haul transmission systems. Therefore, estimating the nonlinear signal-to-noise ratio (SNR) is indispensable to guarantee the quality of transmission and manage the operation of optical networks. The deep neural network (DNN) has been successfully applied for the SNR estimation. However, the performance substantially degrades, when the mega dataset is inaccessible. Here, we demonstrate an accurate SNR estimation based on the data augmentation (DA)-assisted DNN with a small-scale dataset in the frequency domain. When the 95-GBaud dual-polarization 16 quadrature amplitude modulation (DP-16QAM) signal with variable optical launch powers from -2 to 4-dBm is numerically transmitted from 80-km to 1520-km standard single-mode fiber (SSMF), we identify that, in comparison with traditional DNN scheme, the DA allows the reduction of the training dataset size by about 60% while keeping the same mean absolute error (MAE) as 0.2-dB of SNR estimation. Meanwhile, the DA-assisted DNN scheme can reduce the MAE by about 0.14-dB compared with the traditional DNN scheme, when both SNR estimation schemes use 100 raw datasets which contain 700 symbols. Due to these observations, the DA-assisted DNN scheme is promising for field-trial nonlinear SNR estimation, especially when the collection of mega datasets is inconvenient.
光纤非线性是长距离传输系统的主要损伤之一。因此，估计非线性信噪比（SNR）对于保证传输质量和管理光网络的运行是必不可少的。深度神经网络（DNN）已成功应用于SNR估计。然而，当无法获取海量数据集时，其性能会大幅下降。在此，我们展示了一种基于数据增强（DA）辅助的DNN在频域中利用小规模数据集进行准确的SNR估计。当95GBaud双偏振16正交幅度调制（DP-16QAM）信号在-2至4dBm的可变光发射功率下从80km至1520km的标准单模光纤（SSMF）中进行数值传输时，我们发现，与传统DNN方案相比，数据增强可使训练数据集大小减少约60%，同时保持相同的平均绝对误差（MAE），即SNR估计为0.2dB。同时，当两种SNR估计方案都使用包含700个符号的100个原始数据集时，与传统DNN方案相比，DA辅助的DNN方案可将MAE降低约0.14dB。基于这些观察结果，DA辅助的DNN方案在现场试验非线性SNR估计方面具有前景，特别是在收集海量数据集不方便的情况下。
