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.

通过使用PyTorch构建的开源框架OpenDPD，该论文提出了一种新的DGRU-DPD方法，用于修正宽带功率放大器中的非线性问题，并与先前的DPD模型进行了比较，结果表明在DTX架构中，DGRU-DPD在数字PA DPA中表现优异，并且公开了相关的代码、数据集和文档。

OpenDPD: 一个用于宽带功放建模和数字前置失真的开源端到端学习和基准测试框架