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12 November 2024 Channel modeling with one-stage conditional generative adversarial network for fiber optic communication system
Xiaochen Lu, Baotai Yao, Yuhan Tao, Bohao Li, Xiaorui Zong
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Proceedings Volume 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) ; 1339549 (2024) https://doi.org/10.1117/12.3049414
Event: International Conference on Optics, Electronics, and Communication Engineering, 2024, Wuhan, China
Abstract
Effective channel modeling is essential for optimizing and designing modern communication systems. It is especially significant for the next generation of communication systems, which are anticipated to revolutionize data transmission methods. Existing methods for channel modeling, particularly those based on deep learning, suffer from high training complexity and slow deployment, making them less viable for real-time applications. Addressing these challenges, we propose a novel wave-to-wave-level modeling strategy utilizing a one-stage conditional generation countermeasure network (OSCGAN). This method is specifically tailored for a 20G Baud IM/DD optical fiber communication system. Our approach significantly reduces training complexity and expedites the deployment process. We experimentally demonstrate that our model not only achieves lower computational overhead but also maintains higher accuracy across various channel conditions. This advancement presents a promising solution for efficiently deploying advanced communication systems while ensuring robust and accurate performance in diverse operational environments. Through this innovative approach, our study contributes to the field by providing a feasible and efficient alternative for channel modeling in high-speed communication systems
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Xiaochen Lu, Baotai Yao, Yuhan Tao, Bohao Li, and Xiaorui Zong "Channel modeling with one-stage conditional generative adversarial network for fiber optic communication system", Proc. SPIE 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) , 1339549 (12 November 2024); https://doi.org/10.1117/12.3049414
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KEYWORDS
Telecommunications
Education and training
Modeling
Fiber optic communications
Data modeling
Optical networks
Fiber optic networks
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