Team and research¶
OpenDPD is developed by the Lab of Efficient Machine Intelligence at Delft University of Technology. It brings PA modeling, neural digital predistortion and reproducible evaluation into one open research framework.
Team¶
| Contributor | Role |
|---|---|
| Chang Gao | Project Leader |
| Yizhuo Wu | Leading Developer |
| Ang Li | Core Developer |
| Huanqiang Duan | Contributor |
| Ruishen Yang | Contributor |
| Qian Wu | Contributor |
See the GitHub contributors for the evolving contribution history. Studio's About page presents the team, lab, repository link and installed version; it no longer embeds GitHub activity.
Research and citation¶
Start with the OpenDPD framework paper. The citation guide contains BibTeX, related papers and how to cite software, data and leaderboard entries. GitHub's Cite this repository button uses CITATION.cff at the repository root.
Watch the measured OpenDPDv2 demo, then see the feature history, benchmark and reproduction guide for the associated experiments and settings.
Experimental setup¶
The repository includes the original research pipeline and measurement-related MATLAB scripts. The measured DPD guide explains how captured signals are associated with an exported DPD input and evaluated as measured evidence.