# OpenDPD: An Open-Source End-to-End Learning & Benchmarking Framework for Wideband Power Amplifier Modeling and Digital Pre-Distortion

arXiv: https://arxiv.org/abs/2401.08318 · HTML: https://mlsanity.com/paper/2401.08318/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [The unreasonable effectiveness of the forget gate](https://mlsanity.com/paper/1804.04849/) (2018)
- [Energy Efficient Extreme MIMO: Design Goals and Directions](https://mlsanity.com/paper/2301.01119/) (2023)

## Built on by (most cited first, 7 of 7)

- [MP-DPD: Low-Complexity Mixed-Precision Neural Networks for Energy-Efficient Digital Predistortion of Wideband Power Amplifiers](https://mlsanity.com/paper/2404.15364/) (2024)
- [DPD-NeuralEngine: A 22-nm 6.6-TOPS/W/mm2 Recurrent Neural Network Accelerator for Wideband Power Amplifier Digital Pre-Distortion](https://mlsanity.com/paper/2410.11766/) (2024)
- [DeltaDPD: Exploiting Dynamic Temporal Sparsity in Recurrent Neural Networks for Energy-Efficient Wideband Digital Predistortion](https://mlsanity.com/paper/2505.06250/) (2025)
- [TCN-DPD: Parameter-Efficient Temporal Convolutional Networks for Wideband Digital Predistortion](https://mlsanity.com/paper/2506.12165/) (2025)
- [OpenDPDv2: A Unified Learning and Optimization Framework for Neural Network Digital Predistortion](https://mlsanity.com/paper/2507.06849/) (2025)
- [Wideband Power Amplifier Behavioral Modeling Using an Amplitude Conditioned LSTM](https://mlsanity.com/paper/2603.00101/) (2026)
- [Message Passing Based Two-Timescale Bayesian Learning for Joint Channel and Memory Hardware Impairments Tracking](https://mlsanity.com/paper/2607.01660/) (2026)

## Abstract (arXiv)

With the rise in communication capacity, deep neural networks (DNN) for digital pre-distortion (DPD) to correct non-linearity in wideband power amplifiers (PAs) have become prominent. Yet, there is a void in open-source and measurement-setup-independent platforms for fast DPD exploration and objective DPD model comparison. This paper presents an open-source framework, OpenDPD, crafted in PyTorch, with an associated dataset for PA modeling and DPD learning. We introduce a Dense Gated Recurrent Unit (DGRU)-DPD, trained via a novel end-to-end learning architecture, outperforming previous DPD models on a digital PA (DPA) in the new digital transmitter (DTX) architecture with unconventional transfer characteristics compared to analog PAs. Measurements show our DGRU-DPD achieves an ACPR of -44.69/-44.47 dBc and an EVM of -35.22 dB for 200 MHz OFDM signals. OpenDPD code, datasets, and documentation are publicly available at https://github.com/lab-emi/OpenDPD.