Organizers
Tingjun Chen, Duke University, USADan Kilper, Trinity College Dublin, Ireland
Camille Delezoide, Nokia Bell Labs, France
Background, Motivation, and Objective
In recent years, machine learning (ML) has proven to be a transformative tool in modeling and understanding complex real-world data and systems. Its applications have revolutionized fields such as natural language processing, image processing, and radio frequency (RF) systems. In addition to contributions from industry and academia, the scientific advances in these fields have also been driven in part by challenges organized by the broader community, leveraging large-scale datasets such as MNIST, ImageNet, and RF signal classification. Recent breakthroughs have also shown the potential of using ML to improve modeling, performance prediction, and resource provisioning in optical networks across various scales. However, despite the growing number of works, the field still lacks, to the best of our knowledge, universal benchmarks with structured datasets to guide the community in pushing forward the frontier in this research direction.
To bridge this gap, we are excited to launch the Inaugural Dataset Submission at OFC 2026, which aims to solicit datasets and to establish a collaborative platform for researchers, scholars, and practitioners from academia and industry to develop, evaluate, and showcase ML techniques applied to diverse datasets collected from real-world optical devices, platforms, and networks. Participants will have the opportunity to share their collected datasets with the community and gain first-hand experience with these datasets, including detailed hardware specifications, data collection process, and data formatting. We envision that this call for data submissions will aspire to accelerate and foster progress in the integration and use of advanced ML techniques into optical networks, and to pave the way toward smarter, more efficient, and scalable transmission systems that meet the demands of future optical systems and networks.
Topic Categories
- Optical devices and hardware performance data: Gain, noise figure, and spectral characteristics of optical amplifiers; noise and nonlinearity of fibers; laser, modulator, and detector characteristics;
- Signal and quality of transmission data: Optical channel power, OSNR and GSNR, BER, Q-factor, transmission impairments (e.g., chromatic dispersion, nonlinear effects), raw or processed signals with different modulation formats (e.g., PSK, QAM);
- Distributed acoustic, vibration, and temperature sensing data;
- Silicon photonic design data: device simulation, layout and design, experimental characterization;
- Optical network traffic, topology, wavelength and route assignment data;
- Simulators and digital twins for data generation and comparison with measurement data;
- Other types of datasets that might be of interest to the OFC community.