A Modular 1D-CNN Architecture for Real-time Digital Pre-distortion
This work addresses hardware adaptation challenges for real-time DPD in RF systems, representing an incremental improvement in domain-specific applications.
The study tackled real-time linearization of RF power amplifiers using a modular 1D-CNN architecture for digital predistortion, achieving superior performance with 100 MHz signals compared to other neural network methods.
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.