TR2022-007

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

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 1DCNN obtains superior performance compared with other neural network architectures for real-time DPD application.

 

  • Related Publication

  •  De Silva, U., Ma, R., Koike-Akino, T., Yamashita, A., Nakamizo, H., "Modular 1D-CNN Architecture for Real-time Digital Pre-distortion", arXiv, October 2021.
    BibTeX arXiv
    • @article{DeSilva2021oct,
    • author = {De Silva, Udara and Ma, Rui and Koike-Akino, Toshiaki and Yamashita, Ao and Nakamizo, Hideyuki},
    • title = {Modular 1D-CNN Architecture for Real-time Digital Pre-distortion},
    • journal = {arXiv},
    • year = 2021,
    • month = oct,
    • url = {https://arxiv.org/abs/2111.09637}
    • }