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arXiv · 2111.09637

A Modular 1D-CNN Architecture for Real-time Digital Pre-distortion

Udara De Silva·Toshiaki Koike-Akino·Rui Ma·Ao Yamashita·Hideyuki Nakamizo
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Abstract

This study reports a novel hardware-friendly modular architecture for implementing one dimensional convolutional neural network (1D-CNN) digital predistortion (DPD) technique to linearize RF power amplifier (PA) real-time.The modular nature of our design enables DPD system adaptation for variable resource and timing constraints.Our work also presents a co-simulation architecture to verify the DPD performance with an actual power amplifier hardware-in-the-loop.The experimental results with 100 MHz signals show that the proposed 1D-CNN obtains superior performance compared with other neural network architectures for real-time DPD application.

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Udara De Silva, Toshiaki Koike-Akino, Rui Ma, Ao Yamashita, Hideyuki Nakamizo. 2021-11-18. A Modular 1D-CNN Architecture for Real-time Digital Pre-distortion. https://doi.org/10.1109/pawr53092.2022.9719754

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