End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairment

Abstract

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.

Accepted for oral presentation on the Optical Networking and Communication Conference & Exhibition (OFC 2021)

References in corpus (1)

• Deep-learning Autoencoder for Coherent and Nonlinear Optical Communication