# Fiber neural networks for the intelligent optical fiber communications

arXiv: https://arxiv.org/abs/2408.12602 · HTML: https://mlsanity.com/paper/2408.12602/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 7 Aug 2024.

## Builds on (most cited first, 6 of 6)

- [All-Optical Machine Learning Using Diffractive Deep Neural Networks](https://mlsanity.com/paper/1804.08711/) (2018)
- [End-to-end Deep Learning of Optical Fiber Communications](https://mlsanity.com/paper/1804.04097/) (2018)
- [Fast and Accurate Optical Fiber Channel Modeling Using Generative Adversarial Network](https://mlsanity.com/paper/2002.12648/) (2020)
- [Principle-driven Fiber Transmission Model based on PINN Neural Network](https://mlsanity.com/paper/2108.10734/) (2021)
- [Machine-learning custom-made basis functions for partial differential equations](https://mlsanity.com/paper/2111.05307/) (2021)
- [Electro-optical Neural Networks based on Time-stretch Method](https://mlsanity.com/paper/1909.07788/) (2019)

## Abstract (arXiv)

Optical neural networks have long cast attention nowadays. Like other optical structured neural networks, fiber neural networks which utilize the mechanism of light transmission to compute can take great advantages in both computing efficiency and power cost. Though the potential ability of optical fiber was demonstrated via the establishing of fiber neural networks, it will be of great significance of combining both fiber transmission and computing functions so as to cater the needs of future beyond 5G intelligent communication signal processing. Thus, in this letter, the fiber neural networks and their related optical signal processing methods will be both developed. In this way, information derived from the transmitted signals can be directly processed in the optical domain rather than being converted to the electronic domain. As a result, both prominent gains in processing efficiency and power cost can be further obtained. The fidelity of the whole structure and related methods is demonstrated by the task of modulation format recognition which plays important role in fiber optical communications without losing the generality.