End-to-End Learning for VCSEL-based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities
Journal article, 2023

Optical interconnects (OIs) based on vertical-cavity surface-emitting lasers (VCSELs) are the main workhorse within data centers, supercomputers, and even vehicles, providing low-cost, high-rate connectivity. VCSELs must operate under extremely harsh and time-varying conditions, thus requiring adaptive and flexible designs of the communication chain. Such designs can be built based on mathematical models (model-based design) or learned from data (machine learning (ML) based design). Various ML techniques have recently come to the forefront, replacing individual components in the transmitters and receivers with deep neural networks. Beyond such component-wise learning, end-to-end (E2E) autoencoder approaches can reach the ultimate performance through co-optimizing entire parameterized transmitters and receivers. This tutorial paper aims to provide an overview of ML for VCSEL-based OIs, with a focus on E2E approaches, dealing specifically with the unique challenges facing VCSELs, such as the wide temperature variations and complex models.

Machine learning

Optical transmitters

end-to-end learning

Transceivers

Data models

Optical fibers

VCSEL-based optical interconnects

optical communications

Vertical cavity surface emitting lasers

Adaptation models

Optical fiber networks

Author

Muralikrishnan Srinivasan

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Jinxiang Song

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Alexander Grabowski

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

Krzysztof Szczerba

OpenLight Photonics

Holger K. Iversen

Technical University of Denmark (DTU)

Mikkel N. Schmidt

Technical University of Denmark (DTU)

D. Zibar

Technical University of Denmark (DTU)

Jochen Schröder

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

Anders Larsson

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

Christian Häger

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Henk Wymeersch

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Journal of Lightwave Technology

0733-8724 (ISSN) 1558-2213 (eISSN)

Vol. 41 11 3261-3277

Physics-Based Deep Learning for Optical Data Transmission and Distributed Sensing

Swedish Research Council (VR) (2020-04718), 2021-01-01 -- 2024-12-31.

Hot-Optics

Swedish Foundation for Strategic Research (SSF) (CHI19-0004), 2021-01-01 -- 2025-12-31.

Unlocking the Full-dimensional Fiber Capacity

Knut and Alice Wallenberg Foundation (KAW 2018.0090), 2019-07-01 -- 2024-06-30.

Subject Categories

Production Engineering, Human Work Science and Ergonomics

Communication Systems

Embedded Systems

Computer Systems

Areas of Advance

Information and Communication Technology

DOI

10.1109/JLT.2023.3251660

More information

Latest update

12/25/2023