Digital Pre-Distortion (DPD) enhances signal quality in wideband RF power
amplifiers (PAs). As signal bandwidths expand in modern radio systems, DPD's
energy consumption increasingly impacts overall system efficiency. Deep Neural
Networks (DNNs) offer promising advancements in DPD, yet their high complexity
hinders their practical deployment. This paper introduces open-source
mixed-precision (MP) neural networks that employ quantized low-precision
fixed-point parameters for energy-efficient DPD. This approach reduces
computational complexity and memory footprint, thereby lowering power
consumption without compromising linearization efficacy. Applied to a 160MHz-BW
1024-QAM OFDM signal from a digital RF PA, MP-DPD gives no performance loss
against 32-bit floating-point precision DPDs, while achieving -43.75 (L)/-45.27
(R) dBc in Adjacent Channel Power Ratio (ACPR) and -38.72 dB in Error Vector
Magnitude (EVM). A 16-bit fixed-point-precision MP-DPD enables a 2.8X reduction
in estimated inference power. The PyTorch learning and testing code is publicly
available at https://github.com/lab-emi/OpenDPD.

应用混合精度神经网络降低功耗的数字预失真（DPD）技术，在无线电系统中实现了高效的信号线性化、降低能耗，并取得了与 32-bit 浮点精度 DPDs 相当的性能指标。

MP-DPD: 低复杂度的混合精度神经网络用于宽带功率放大器的高能效数字预畸变

MP-DPD: Low-Complexity Mixed-Precision Neural Networks for  Energy-Efficient Digital Predistortion of Wideband Power Amplifiers

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 this https URL

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。