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.
Look-up table
Transmitter predistortion
Machine learning
optical communication
Author
Zonglong He
Chalmers, Microtechnology and Nanoscience (MC2), Photonics
Jinxiang Song
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Christian Häger
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
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
Henk Wymeersch
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Jochen Schröder
Chalmers, Microtechnology and Nanoscience (MC2), Photonics
2021 European Conference on Optical Communication, ECOC 2021
9605892
978-1-6654-3868-1 (ISBN)
47th European Conference on Optical Communications, ECOC 2021
Bordeaux, France,
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
ISBN
9781665438681