Recent Advances on Machine Learning-aided DSP for Short-reach and Long-haul Optical Communications

TL;DR

This paper reviews recent progress in applying machine learning techniques to enhance digital signal processing in optical communications, focusing on equalizer algorithms and hardware implementations for both short-reach and long-haul systems.

Contribution

It provides a comprehensive overview of recent algorithmic and hardware advancements in ML-aided DSP for optical communications, emphasizing both conventional and neuromorphic approaches.

Findings

Improved equalization algorithms using machine learning.

Implementation of ML-based DSP on neuromorphic hardware.

Enhanced performance in optical communication systems.

Abstract

In this paper, we highlight recent advances in the use of machine learning for implementing equalizers for optical communications. We highlight both algorithmic advances as well as implementation aspects using conventional and neuromorphic hardware.