We propose an end-to-end learning-based approach for superchannel systems impaired by non-ideal hardware component. Our system achieves up to 60% SER reduction and up to 50% guard band reduction compared with the considered baseline scheme.
@article{arxiv.2103.15856,
title = {End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairment},
author = {Jinxiang Song and Christian Häger and Jochen Schröder and Alexandre Graell i Amat and Henk Wymeersch},
journal= {arXiv preprint arXiv:2103.15856},
year = {2021}
}
Comments
Accepted for oral presentation on the Optical Networking and Communication Conference & Exhibition (OFC 2021)