论文标题
使用生成对抗网络快速准确的光纤通道建模
Fast and Accurate Optical Fiber Channel Modeling Using Generative Adversarial Network
论文作者
论文摘要
在这项工作中，研究了一种新的数据驱动的光纤通道建模方法，研究了生成对抗网络（GAN），以了解光纤通道传输功能的分布。我们的研究重点是衰减，铬色散，自相度调制（SPM）和扩增的自发发射（ASE）噪声的联合通道影响。为了实现GAN用于通道建模的成功，我们修改了损耗函数，设计输入的条件向量并解决了长途传输的模式崩溃。文章还显示了GAN的有效架构，参数和培训技巧。结果表明，所提出的方法可以学习光纤通道的准确传输函数。建模的传输距离可以高达1000公里，并且可以将其扩展到理论上的任意距离。此外，GAN在不同的光学发射功率，调制格式和输入信号分布下显示出强大的概括能力。将GAN与拆分型傅立叶方法（SSFM）进行比较，总乘法数仅为SSFM的2％，运行时间在1000公里的传输中少于0.1秒，而使用SSFM在同一硬件和软件下使用SSFM的400秒，这突显了fiber Channel模型的复杂性降低。
In this work, a new data-driven fiber channel modeling method, generative adversarial network (GAN) is investigated to learn the distribution of fiber channel transfer function. Our investigation focuses on joint channel effects of attenuation, chromic dispersion, self-phase modulation (SPM), and amplified spontaneous emission (ASE) noise. To achieve the success of GAN for channel modeling, we modify the loss function, design the condition vector of input and address the mode collapse for the long-haul transmission. The effective architecture, parameters, and training skills of GAN are also displayed in the paper. The results show that the proposed method can learn the accurate transfer function of the fiber channel. The transmission distance of modeling can be up to 1000 km and can be extended to arbitrary distance theoretically. Moreover, GAN shows robust generalization abilities under different optical launch powers, modulation formats, and input signal distributions. Comparing the complexity of GAN with the split-step Fourier method (SSFM), the total multiplication number is only 2% of SSFM and the running time is less than 0.1 seconds for 1000-km transmission, versus 400 seconds using the SSFM under the same hardware and software conditions, which highlights the remarkable reduction in complexity of the fiber channel modeling.
