End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairment
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Mar 2021.
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Citing papers per year: 2021: 2; 2022: 2.
4 papers.
- 2021Waveform Learning for Next-Generation Wireless Communication Systemscited by 10
- 2021End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Modelcited by 2
- 2022Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communicationcited by 2
- 2022Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learningcited by 2
- 2022Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learning8 Nov 2022
- 2022Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication5 Apr 2022
- 2021Waveform Learning for Next-Generation Wireless Communication Systems2 Sep 2021
- 2021End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model26 Jul 2021