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5 March 2021 Modeling, monitoring, and self-learning techniques for building an AI-driven digital twin optical system
Qunbi Zhuge
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Proceedings Volume 11713, Next-Generation Optical Communication: Components, Sub-Systems, and Systems X; 117130M (2021) https://doi.org/10.1117/12.2583091
Event: SPIE OPTO, 2021, Online Only
Abstract
Artificial intelligence (AI) has shown significant performance in optical network control and management. However, the reliability, complexity and deployment procedure of these AI-based applications need further investigation. To efficiently speed up the network automation and function extension, a digital-twin-based network control framework is proposed, which can intelligently synchronize with the practical system to support the upper-layer applications. To build a digital twin, high efficiency modeling, monitoring and self-learning mechanisms are the key building blocks. In this paper, we discuss our recent works on modeling, monitoring and self-learning methods for building a digital-twin for optical networks.
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Qunbi Zhuge "Modeling, monitoring, and self-learning techniques for building an AI-driven digital twin optical system", Proc. SPIE 11713, Next-Generation Optical Communication: Components, Sub-Systems, and Systems X, 117130M (5 March 2021); https://doi.org/10.1117/12.2583091
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KEYWORDS
Artificial intelligence
Optical networks
Control systems
Reliability
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