# Differentiable Machine Learning-Based Modeling for Directly-Modulated Lasers

arXiv: https://arxiv.org/abs/2309.15747 · HTML: https://mlsanity.com/paper/2309.15747/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2023.

## Built on by (most cited first, 2 of 2)

- [Experimental End-to-End Optimization of Directly Modulated Laser-based IM/DD Transmission](https://mlsanity.com/paper/2508.19910/) (2025)
- [Machine Learning Models to Identify Promising Nested Antiresonance Nodeless Fiber Designs](https://mlsanity.com/paper/2603.13302/) (2026)

## Abstract (arXiv)

End-to-end learning has become a popular method for joint transmitter and receiver optimization in optical communication systems. Such approach may require a differentiable channel model, thus hindering the optimization of links based on directly modulated lasers (DMLs). This is due to the DML behavior in the large-signal regime, for which no analytical solution is available. In this paper, this problem is addressed by developing and comparing differentiable machine learning-based surrogate models. The models are quantitatively assessed in terms of root mean square error and training/testing time. Once the models are trained, the surrogates are then tested in a numerical equalization setup, resembling a practical end-to-end scenario. Based on the numerical investigation conducted, the convolutional attention transformer is shown to outperform the other models considered.