  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.
