Concept and Experimental Demonstration of Optical IM/DD End-to-End System Optimization using a Generative Model
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Abstract (arXiv)
We perform an experimental end-to-end transceiver optimization via deep learning using a generative adversarial network to approximate the test-bed channel. Previously, optimization was only possible through a prior assumption of an explicit simplified channel model.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Dec 2019.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 2; 2025: 1.
6 papers.
- 2021End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Modelcited by 2
- 2022Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learningcited by 2
- 2022Blind and Channel-agnostic Equalization Using Adversarial Networkscited by 1
- 2024On the Robustness of Deep Learning-aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledgecited by 1
- 2024Data-Driven Symbol Detection for Intersymbol Interference Channels with Bursty Impulsive Noisecited by 1
- 2025Experimental End-to-End Optimization of Directly Modulated Laser-based IM/DD Transmissioncited by 0
- 2025Experimental End-to-End Optimization of Directly Modulated Laser-based IM/DD Transmission27 Aug 2025
- 2024Data-Driven Symbol Detection for Intersymbol Interference Channels with Bursty Impulsive Noise17 May 2024
- 2024On the Robustness of Deep Learning-aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledge23 Jan 2024
- 2022Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learning8 Nov 2022
- 2022Blind and Channel-agnostic Equalization Using Adversarial Networks15 Sep 2022
- 2021End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model26 Jul 2021