# Experimental End-to-End Optimization of Directly Modulated Laser-based IM/DD Transmission

arXiv: https://arxiv.org/abs/2508.19910 · HTML: https://mlsanity.com/paper/2508.19910/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 27 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Model-Based End-to-End Learning for Multi-Target Integrated Sensing and Communication under Hardware Impairments](https://mlsanity.com/paper/2307.04111/) (2023)
- [Concept and Experimental Demonstration of Optical IM/DD End-to-End System Optimization using a Generative Model](https://mlsanity.com/paper/1912.05146/) (2019)
- [Gradient-free training of autoencoders for non-differentiable communication channels](https://mlsanity.com/paper/2012.11227/) (2020)
- [Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learning](https://mlsanity.com/paper/2211.04311/) (2022)
- [End-to-end Optimization of Constellation Shaping for Wiener Phase Noise Channels with a Differentiable Blind Phase Search](https://mlsanity.com/paper/2212.03839/) (2022)
- [Differentiable Machine Learning-Based Modeling for Directly-Modulated Lasers](https://mlsanity.com/paper/2309.15747/) (2023)
- [End-to-end Optimization of Optical Communication Systems based on Directly Modulated Lasers](https://mlsanity.com/paper/2405.09907/) (2024)
- [End-to-End Learning of Transmitter and Receiver Filters in Bandwidth Limited Fiber Optic Communication Systems](https://mlsanity.com/paper/2409.11980/) (2024)
- [Experimental Demonstration of End-to-End Optimization for Directly Modulated Laser-based IM/DD Systems](https://mlsanity.com/paper/2410.17805/) (2024)

## Abstract (arXiv)

Directly modulated lasers (DMLs) are an attractive technology for short-reach intensity modulation and direct detection communication systems. However, their complex nonlinear dynamics make the modeling and optimization of DML-based systems challenging. In this paper, we study the end-to-end optimization of DML-based systems based on a data-driven surrogate model trained on experimental data. The end-to-end optimization includes the pulse shaping and equalizer filters, the bias current and the modulation radio-frequency (RF) power applied to the laser. The performance of the end-to-end optimization scheme is tested on the experimental setup and compared to 4 different benchmark schemes based on linear and nonlinear receiver-side equalization. The results show that the proposed end-to-end scheme is able to deliver better performance throughout the studied symbol rates and transmission distances while employing lower modulation RF power, fewer filter taps and utilizing a smaller signal bandwidth.