Wideband Time-Domain Digital Backpropagation via Subband Processing and Deep Learning

TL;DR

This paper introduces a low-complexity, subband-based digital backpropagation method utilizing deep learning to compensate for walk-off effects, demonstrating significant SNR improvements in high-speed optical links.

Contribution

It presents a novel subband processing architecture combined with deep learning for efficient digital backpropagation in optical communications.

Findings

Achieves 2.8 dB SNR improvement over linear equalization.

Effectively compensates walk-off with simple delay elements.

Reduces computational complexity of digital backpropagation.

Abstract

We propose a low-complexity sub-banded DSP architecture for digital backpropagation where the walk-off effect is compensated using simple delay elements. For a simulated 96-Gbaud signal and 2500 km optical link, our method achieves a 2.8 dB SNR improvement over linear equalization.