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 +3 more
11/15/2024
eess.SPcs.LG

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey

89% match2024

Equalization in Dispersion-Managed Systems Using Learned Digital Back-Propagation

83% match2023

CNN-Based Equalization for Communications: Achieving Gigabit Throughput with a Flexible FPGA Hardware Architecture

82% match2024

Artificial Neural Networks for Photonic Applications: From Algorithms to Implementation

82% match2024

Hardware Realization of Nonlinear Activation Functions for NN-based Optical Equalizers

81% match2023

Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systems

81% match2023

Cite this paper

@article{schmalen2024recent,
  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},
  year   = {2024},
  eprint = {2411.10101},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2411.10101v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2411.10101.svg)](https://codeofpaper.com/paper/2411.10101)

Discussion