# OpenDPDv2: A Unified Learning and Optimization Framework for Neural Network Digital Predistortion

arXiv: https://arxiv.org/abs/2507.06849 · HTML: https://mlsanity.com/paper/2507.06849/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 9 Jul 2025.

## Builds on (most cited first, 13 of 13)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [A White Paper on Neural Network Quantization](https://mlsanity.com/paper/2106.08295/) (2021)
- [Delta Networks for Optimized Recurrent Network Computation](https://mlsanity.com/paper/1612.05571/) (2016)
- [The gem5 Simulator: Version 20.0+](https://mlsanity.com/paper/2007.03152/) (2020)
- [OpenDPD: An Open-Source End-to-End Learning & Benchmarking Framework for Wideband Power Amplifier Modeling and Digital Pre-Distortion](https://mlsanity.com/paper/2401.08318/) (2024)
- [Energy Efficient Extreme MIMO: Design Goals and Directions](https://mlsanity.com/paper/2301.01119/) (2023)
- [MP-DPD: Low-Complexity Mixed-Precision Neural Networks for Energy-Efficient Digital Predistortion of Wideband Power Amplifiers](https://mlsanity.com/paper/2404.15364/) (2024)
- [Convolutional Neural Network for Behavioral Modeling and Predistortion of Wideband Power Amplifiers](https://mlsanity.com/paper/2005.09848/) (2020)
- [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)
- [SparseDPD: A Sparse Neural Network-based Digital Predistortion FPGA Accelerator for RF Power Amplifier Linearization](https://mlsanity.com/paper/2506.16591/) (2025)

## Abstract (arXiv)

Neural network (NN)-based Digital Predistortion (DPD) improves linearization for wideband radio frequency (RF) power amplifiers (PAs) but often increases the complexity of the digital back-end. This paper presents OpenDPDv2, an open-source end-to-end framework that unifies PA modeling, NN-DPD learning, and deployment-oriented optimization. OpenDPDv2 introduces temporal residual (TRes)-Delta Gated Recurrent Unit (DeltaGRU), a delta-RNN DPD architecture with a lightweight temporal residual path for robust operation under aggressive temporal sparsity, and supports joint optimization with fixed-point quantization. On a 3.5 GHz GaN Doherty PA driven by a TM3.1a 200 MHz 256-QAM OFDM signal, the FP32 TRes-DeltaGRU model achieves -59.9 dBc Adjacent Channel Power Ratio (ACPR) and -42.1 dB Error Vector Magnitude (EVM). With 56% temporal sparsity and W12A12 quantization, the model uses 450 active parameters while maintaining -51.8 dBc ACPR and -35.2 dB EVM. Timing-accurate Gem5-based evaluation further indicates a 4.5x reduction in forward-pass energy for this deployment-oriented operating point. Code, datasets, and documentation are publicly available at https://github.com/lab-emi/OpenDPD.