# DeltaDPD: Exploiting Dynamic Temporal Sparsity in Recurrent Neural Networks for Energy-Efficient Wideband Digital Predistortion

arXiv: https://arxiv.org/abs/2505.06250 · HTML: https://mlsanity.com/paper/2505.06250/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 29 Apr 2025.

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

- [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)
- [Delta Networks for Optimized Recurrent Network Computation](https://mlsanity.com/paper/1612.05571/) (2016)
- [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)

## Built on by (most cited first, 1 of 1)

- [OpenDPDv2: A Unified Learning and Optimization Framework for Neural Network Digital Predistortion](https://mlsanity.com/paper/2507.06849/) (2025)

## Abstract (arXiv)

Digital Predistortion (DPD) is a popular technique to enhance signal quality in wideband RF power amplifiers (PAs). With increasing bandwidth and data rates, DPD faces significant energy consumption challenges during deployment, contrasting with its efficiency goals. State-of-the-art DPD models rely on recurrent neural networks (RNN), whose computational complexity hinders system efficiency. This paper introduces DeltaDPD, exploring the dynamic temporal sparsity of input signals and neuronal hidden states in RNNs for energy-efficient DPD, reducing arithmetic operations and memory accesses while preserving satisfactory linearization performance. Applying a TM3.1a 200MHz-BW 256-QAM OFDM signal to a 3.5 GHz GaN Doherty RF PA, DeltaDPD achieves -50.03 dBc in Adjacent Channel Power Ratio (ACPR), -37.22 dB in Normalized Mean Square Error (NMSE) and -38.52 dBc in Error Vector Magnitude (EVM) with 52% temporal sparsity, leading to a 1.8X reduction in estimated inference power. The DeltaDPD code will be released after formal publication at https://www.opendpd.com.