Symbol-Based Supervised Learning Predistortion for Compensating Transmitter Nonlinearity
Paper in proceeding, 2021

We experimentally demonstrate a symbol-based nonlinear digital predistortion (DPD) technique utilizing supervised learning, which is robust against a change of modulation format. Back-to-back transmission of 30 Gbaud 32, 64 and 256QAM confirms that our scheme significantly outperforms the baseline of arcsine-based predistortion.

Transmitter predistortion

Look-up table

optical communication

Machine learning

Author

Zonglong He

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

Jinxiang Song

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks, Communication Systems

Christian Häger

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks, Communication Systems

Kovendhan Vijayan

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

Peter Andrekson

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

Magnus Karlsson

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

Alexandre Graell I Amat

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks, Communication Systems

Henk Wymeersch

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks, Communication Systems

Jochen Schröder

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

2021 European Conference on Optical Communications, ECOC 2021

9605892
978-1-6654-3868-1 (ISBN)

47th European Conference on Optical Communications, ECOC 2021
Bordeaux, France,

Unlocking the Full-dimensional Fiber Capacity

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

Subject Categories

Other Physics Topics

Communication Systems

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/ECOC52684.2021.9605892

More information

Latest update

1/19/2022