# Concept and Experimental Demonstration of Optical IM/DD End-to-End System Optimization using a Generative Model

arXiv: https://arxiv.org/abs/1912.05146 · HTML: https://mlsanity.com/paper/1912.05146/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

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

- [End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model](https://mlsanity.com/paper/2107.12320/) (2021)
- [Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learning](https://mlsanity.com/paper/2211.04311/) (2022)
- [Blind and Channel-agnostic Equalization Using Adversarial Networks](https://mlsanity.com/paper/2209.07277/) (2022)
- [On the Robustness of Deep Learning-aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledge](https://mlsanity.com/paper/2401.12645/) (2024)
- [Data-Driven Symbol Detection for Intersymbol Interference Channels with Bursty Impulsive Noise](https://mlsanity.com/paper/2405.10814/) (2024)
- [Experimental End-to-End Optimization of Directly Modulated Laser-based IM/DD Transmission](https://mlsanity.com/paper/2508.19910/) (2025)

## Abstract (arXiv)

We perform an experimental end-to-end transceiver optimization via deep learning using a generative adversarial network to approximate the test-bed channel. Previously, optimization was only possible through a prior assumption of an explicit simplified channel model.