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

Laurent Schmalen; Vincent Lauinger; Jonas Ney; Norbert Wehn; Patrick Matalla; Sebastian Randel; Alexander von Bank; Eike-Manuel Edelmann

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

Signal Processing 2024-11-18 v1 Machine Learning

Authors: Laurent Schmalen , Vincent Lauinger , Jonas Ney , Norbert Wehn , Patrick Matalla , Sebastian Randel , Alexander von Bank , Eike-Manuel Edelmann

View on arXiv ↗ PDF ↗

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.

Keywords

machine learning-aided DSP optical communications equalizers short-reach optical communications long-haul optical communications neuromorphic hardware algorithmic advances

Cite

@article{arxiv.2411.10101,
  title  = {Recent Advances on Machine Learning-aided DSP for Short-reach and Long-haul Optical Communications},
  author = {Laurent Schmalen and Vincent Lauinger and Jonas Ney and Norbert Wehn and Patrick Matalla and Sebastian Randel and Alexander von Bank and Eike-Manuel Edelmann},
  journal= {arXiv preprint arXiv:2411.10101},
  year   = {2024}
}

Comments

paper accompanying an invited presentation at OFC 2025

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

Abstract

Keywords

Cite

Comments

Related papers