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

arXiv: https://arxiv.org/abs/2111.09637 · HTML: https://mlsanity.com/paper/2111.09637/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 18 Nov 2021.

## Builds on (most cited first, 2 of 2)

- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Convolutional Neural Network for Behavioral Modeling and Predistortion of Wideband Power Amplifiers](https://mlsanity.com/paper/2005.09848/) (2020)

## Abstract (arXiv)

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.