用深度学习设计高效射频功放，提升信号效率与线性度。
Deep Learning-Driven Black-Box Doherty Power Amplifier with Pixelated Output Combiner and Extended Efficiency Range
- 用CNN建模电磁特性，快速预测多端口像素化网络性能。
- 实测峰值效率超74%，9dB回退时仍保持52%效率。
- 适合射频电路设计、功率放大器优化的工程师与研究者。
本文提出一种基于深度学习的逆向设计方法，用于具有多端口像素化输出合成网络的杜赫蒂功率放大器（PA）。开发并训练了一种深度卷积神经网络（CNN），作为电磁（EM）代理模型，以准确且快速地预测像素化无源网络的S参数。利用该CNN代理模型嵌入黑箱杜赫蒂框架，并结合遗传算法（GA）优化器，有效设计出复杂结构的杜赫蒂合成器，实现全对称器件下的扩展回退效率范围。作为概念验证，设计并制造了两款采用三端口像素化合成器的杜赫蒂PA原型，使用GaN HEMT晶体管实现。实测显示，两款原型在2.75 GHz下均达到超过74%的最高漏极效率，输出功率超过44.1 dBm；在相同频率下，9 dB回退功率水平时仍保持超过52%的漏极效率。为评估真实信号条件下的线性度与效率，使用峰值平均功率比（PAPR）为9.0 dB的20 MHz 5G NR类波形进行测试。经数字预失真（DPD）校正后，每款设计平均功率附加效率（PAE）均超过51%，邻道泄漏比（ACLR）优于-60.8 dBc。
原文摘要 · Abstract (English)
This article presents a deep learning-driven inverse design methodology for Doherty power amplifiers (PA) with multi-port pixelated output combiner networks. A deep convolutional neural network (CNN) is developed and trained as an electromagnetic (EM) surrogate model to accurately and rapidly predict the S-parameters of pixelated passive networks. By leveraging the CNN-based surrogate model within a blackbox Doherty framework and a genetic algorithm (GA)-based optimizer, we effectively synthesize complex Doherty combiners that enable an extended back-off efficiency range using fully symmetrical devices. As a proof of concept, we designed and fabricated two Doherty PA prototypes incorporating three-port pixelated combiners, implemented with GaN HEMT transistors. In measurements, both prototypes demonstrate a maximum drain efficiency exceeding 74% and deliver an output power surpassing 44.1 dBm at 2.75 GHz. Furthermore, a measured drain efficiency above 52% is maintained at the 9-dB back-off power level for both prototypes at the same frequency. To evaluate linearity and efficiency under realistic signal conditions, both prototypes are tested using a 20-MHz 5G new radio (NR)-like waveform exhibiting a peak-to-average power ratio (PAPR) of 9.0 dB. After applying digital predistortion (DPD), each design achieves an average power added efficiency (PAE) above 51%, while maintaining an adjacent channel leakage ratio (ACLR) better than -60.8 dBc.
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