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.

翻译：随着通信容量的提升，用于校正宽带功率放大器（PA）非线性的深度神经网络（DNN）数字预失真（DPD）技术日益重要。然而，目前仍缺乏独立于测量设置的开源平台，以实现快速DPD探索与客观DPD模型比较。本文提出一个基于PyTorch构建的开源框架OpenDPD，并配套提供用于PA建模与DPD学习的数据集。我们引入了一种密集门控循环单元（DGRU）DPD模型，通过新颖的端到端学习架构进行训练，在具有与模拟PA非常规传输特性的新型数字发射机（DTX）架构中的数字PA DPA上，性能超越现有DPD模型。实测结果表明，针对200 MHz OFDM信号，我们的DGRU-DPD实现了-44.69/-44.47 dBc的邻道功率比（ACPR）和-35.22 dB的误差矢量幅度（EVM）。OpenDPD代码、数据集及文档已开源，获取地址为https://github.com/lab-emi/OpenDPD。