A digital twin creates a virtual model of a physical system to understand it, predict its evolution, and optimize it while it operates. Digital twins are receiving increasing attention and have been used in a wide range of fields, i.e., from the manufacturing industry to electrical power systems and from aerospace engineering to smart cities, to name a few.
With the proliferation of elastic and programmable optical transceivers, high-order modulation formats, flexible grids, and intelligent orchestration layers, optical networks are rapidly evolving in the direction of openness and disaggregation, flexible transmission, function virtualization, and further automation/autonomy. Typically, optical networks are operated rather statically, while the increase in complexity and flexibility hinders their dynamic and automated adaptation.
This panel aims to present state-of-the-art research activities on the vital role that digital twins can play in alleviating the plethora of challenges inherent in designing and operating complex single-vendor or disaggregated optical networks. Digital twins have the potential to bridge the gap between the network management/control, and the actual physical system, providing a means to understand, predict, and evaluate the behavior and performance of the network as it operates.
Topics to be targeted by the panel include but will not be limited to:
- Fault prediction, detection, identification and localization
- Evaluation of fault mitigation actions
- Evaluation of what-if scenarios for channel and network optimization
- Physical layer (evolution) emulation and QoT estimation
- Evaluation, processing, and understanding of the effects of dynamic actions on the physical layer • Application to optical transport networks and Industry 4.0
Kostas Christodoulopoulos, University of Athens, Greece
Yvan Pointurier, Huawei, France
Chongjin Xie, Alibaba Group, United States
David Boertjes, Ciena, United States
Sai Chen, Alibaba, China
Yann Frignac, Huawei, China
Darli Mello, University of Campinas, Brazil
Shiqui Shen, China Unicom, China