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，目前缺乏开源且独立于测量设置的快速DPD探索及客观模型比较平台。本文提出基于PyTorch构建的开源框架OpenDPD，并附带用于PA建模和DPD学习的数据集。我们引入一种密集门控循环单元（DGRU）-DPD，通过新型端到端学习架构训练，在新型数字发射机（DTX）架构的数字PA（DPA）上（相较于模拟PA具有非常规传输特性），性能超越了此前DPD模型。实测表明，针对200 MHz OFDM信号，我们的DGRU-DPD实现了-44.69/-44.47 dBc的ACPR与-35.22 dB的EVM。OpenDPD代码、数据集及文档已在https://github.com/lab-emi/OpenDPD 公开。