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

TL;DR

This paper introduces a modular 1D-CNN architecture optimized for real-time digital predistortion of RF power amplifiers, demonstrating superior performance and adaptability in hardware implementations.

Contribution

The paper presents a novel hardware-friendly modular 1D-CNN design for real-time DPD, enabling system adaptation and verification with hardware-in-the-loop testing.

Findings

Superior performance over other neural networks in real-time DPD

Effective hardware implementation with modular architecture

Validated with 100 MHz signals in hardware-in-the-loop

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.