Fast and Accurate Optical Fiber Channel Modeling Using Generative Adversarial Network

In this work, a new data-driven fiber channel modeling method, generative adversarial network (GAN) is investigated to learn the distribution of transfer function. Our investigation focuses on joint effects attenuation, chromic dispersion, self-phase modulation (SPM), and amplified spontaneous emission (ASE) noise. To achieve success GAN for modeling, we modify loss function, design condition vector input address mode collapse long-haul transmission. The effective architecture, parameters, training skills are also displayed in paper. results show that proposed method can accurate function channel. transmission distance be up 1000 km extended arbitrary theoretically. Moreover, shows robust generalization abilities under different optical launch powers, formats, signal distributions. Comparing complexity with split-step Fourier (SSFM), total multiplication number only 2% SSFM running time less than 0.1 seconds 1000-km transmission, versus 400 using same hardware software conditions, which highlights remarkable reduction modeling.