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
arXiv:2411.10101v16 citationsh-index: 7OFC
Originality Synthesis-oriented
AI Analysis
It addresses the problem of improving signal processing efficiency in optical communications for researchers and engineers, but is incremental as it focuses on summarizing existing work rather than introducing new methods.
This paper reviews recent advances in using machine learning to implement equalizers for optical communications, covering both algorithmic improvements and hardware implementations including conventional and neuromorphic systems.
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.