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Citing OpenDPD, a board version and an entry

The software. CITATION.cff at the repository root is what GitHub's "Cite this repository" reads; it names the software (version, licence, repository) and the preferred paper:

Y. Wu, G. D. Singh, M. Beikmirza, L. C. N. de Vreede, M. Alavi and C. Gao, "OpenDPD: An Open-Source End-to-End Learning & Benchmarking Framework for Wideband Power Amplifier Modeling and Digital Pre-Distortion," 2024 IEEE International Symposium on Circuits and Systems (ISCAS), 2024, pp. 1–5, doi:10.1109/ISCAS58744.2024.10558162.

BibTeX for the framework, MP-DPD and DeltaDPD is collected below. Related work includes OpenDPDv2 and TCN-DPD; use the publication metadata for the version you cite.

A board version. A board is a file whose hash covers every entry; cite the board id, the version, the hash and the date you read it, so a reader finds exactly what you saw even after later versions exist:

OpenDPD leaderboard opendpd-pa-modeling, version v2026.09, board hash <board_sha256>, docs/leaderboard/v2026.09/pa_modeling.md, accessed 2026-09-06.

The hash is the last line of the Markdown rendering and the board_sha256 field of the JSON. Say the label the board carried (reference benchmark or community leaderboard): the label is computed from the entries and can change between versions.

An entry. Cite the submitter's own citation statement (it is on the card and in the board JSON), plus the entry id and the board version. A retracted or corrected entry keeps its history on the board; cite the version you used.

Data. The built-in datasets are cited through the papers above; a submission's own data is cited as its data card says.

Paper BibTeX

If you find this repository helpful, please cite our work:

  • [ISCAS 2024] OpenDPD: An Open-Source End-to-End Learning & Benchmarking Framework for Wideband Power Amplifier Modeling and Digital Pre-Distortion 

    @INPROCEEDINGS{Wu2024ISCAS,
  author={Wu, Yizhuo and Singh, Gagan Deep and Beikmirza, Mohammadreza and de Vreede, Leo C. N. and Alavi, Morteza and Gao, Chang},
  booktitle={2024 IEEE International Symposium on Circuits and Systems (ISCAS)},
  title={OpenDPD: An Open-Source End-to-End Learning & Benchmarking Framework for Wideband Power Amplifier Modeling and Digital Pre-Distortion},
  year={2024},
  volume={},
  number={},
  pages={1-5},
  keywords={Codes;Transmitters;OFDM;Power amplifiers;Artificial neural networks;Documentation;Benchmark testing;digital pre-distortion;behavioral modeling;deep neural network;power amplifier;digital transmitter},
  doi={10.1109/ISCAS58744.2024.10558162}}

  • [IMS/MWTL 2024] MP-DPD: Low-Complexity Mixed-Precision Neural Networks for Energy-Efficient Digital Pre-distortion of Wideband Power Amplifiers 

    @ARTICLE{Wu2024IMS,
  author={Wu, Yizhuo and Li, Ang and Beikmirza, Mohammadreza and Singh, Gagan Deep and Chen, Qinyu and de Vreede, Leo C. N. and Alavi, Morteza and Gao, Chang},
  journal={IEEE Microwave and Wireless Technology Letters},
  title={MP-DPD: Low-Complexity Mixed-Precision Neural Networks for Energy-Efficient Digital Predistortion of Wideband Power Amplifiers},
  year={2024},
  volume={},
  number={},
  pages={1-4},
  keywords={Deep neural network (DNN);digital predistortion (DPD);digital transmitter (DTX);power amplifier (PA);quantization},
  doi={10.1109/LMWT.2024.3386330}}

  • [IMS/MWTL 2025] DeltaDPD: Exploiting Dynamic Temporal Sparsity in Recurrent Neural Networks for Energy-Efficient Wideband Digital Predistortion 

    @article{Wu2025MWTL,
   title={DeltaDPD: Exploiting Dynamic Temporal Sparsity in Recurrent Neural Networks for Energy-Efficient Wideband Digital Predistortion},
   ISSN={2771-957X},
   url={http://dx.doi.org/10.1109/LMWT.2025.3565004},
   DOI={10.1109/lmwt.2025.3565004},
   journal={IEEE Microwave and Wireless Technology Letters},
   publisher={Institute of Electrical and Electronics Engineers (IEEE)},
   author={Wu, Yizhuo and Zhu, Yi and Qian, Kun and Chen, Qinyu and Zhu, Anding and Gajadharsing, John and de Vreede, Leo C. N. and Gao, Chang},
   year={2025},
   pages={1–4} }