# Fast and Accurate Optical Fiber Channel Modeling Using Generative Adversarial Network

arXiv: https://arxiv.org/abs/2002.12648 · HTML: https://mlsanity.com/paper/2002.12648/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2020.

## Built on by (most cited first, 6 of 6)

- [Fast and accurate waveform modeling of long-haul multi-channel optical fiber transmission using a hybrid model-data driven scheme](https://mlsanity.com/paper/2201.05502/) (2022)
- [Blind and Channel-agnostic Equalization Using Adversarial Networks](https://mlsanity.com/paper/2209.07277/) (2022)
- [Fiber Transmission Model with Parameterized Inputs based on GPT-PINN Neural Network](https://mlsanity.com/paper/2408.09947/) (2024)
- [Principle Driven Parameterized Fiber Model based on GPT-PINN Neural Network](https://mlsanity.com/paper/2408.09951/) (2024)
- [Fiber neural networks for the intelligent optical fiber communications](https://mlsanity.com/paper/2408.12602/) (2024)
- [Artificial Intelligence Reshapes Microwave Photonics](https://mlsanity.com/paper/2605.21224/) (2026)

## Abstract (arXiv)

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.