no code implementations • 18 Nov 2021 • Udara De Silva, Toshiaki Koike-Akino, Rui Ma, Ao Yamashita, Hideyuki Nakamizo
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.
