Model-Based End-to-End Learning for WDM Systems With Transceiver Hardware Impairments
Journal article, 2022

We propose an AE-based transceiver for a WDM system impaired by hardware imperfections. We design our AE following the architecture of conventional communication systems. This enables to initialize the AE-based transceiver to have similar performance to its conventional counterpart prior to training and improves the training convergence rate. We first train the AE in a single-channel system, and show that it achieves performance improvements by putting energy outside the desired bandwidth, and therefore cannot be used for a WDM system. We then train the AE in a WDM setup. Simulation results show that the proposed AE significantly outperforms the conventional approach. More specifically, it increases the spectral efficiency of the considered system by reducing the guard band by 37% and 50% for a root-raised-cosine filter-based matched filter with 10% and 1% roll-off, respectively. An ablation study indicates that the performance gain can be ascribed to the optimization of the symbol mapper, the pulse-shaping filter, and the symbol demapper. Finally, we use reinforcement learning to learn the pulse-shaping filter under the assumption that the channel model is unknown. Simulation results show that the reinforcement-learning-based algorithm achieves similar performance to the standard supervised end-to-end learning approach assuming perfect channel knowledge.

Training

Low-pass filters

Hardware

end-to-end learning

Bandwidth

wavelengthdivision multiplexing

digital signal processing

deep learning

reinforcement learning

Transceivers

Wavelength division multiplexing

Receivers

Autoencoders

Author

Jinxiang Song

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Christian Häger

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Jochen Schröder

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

IEEE Journal of Selected Topics in Quantum Electronics

1077-260X (ISSN) 15584542 (eISSN)

Vol. 28 4 7700114

Unlocking the Full-dimensional Fiber Capacity

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

Subject Categories

Computer Engineering

Communication Systems

Embedded Systems

DOI

10.1109/JSTQE.2022.3163474

More information

Latest update

12/27/2022