Dynamic end-to-end optimization for quality of transmission in reconfigurable transparent optical networks: erratum

2009 ◽  
Vol 8 (5) ◽  
pp. 504
Author(s):  
Guanjun Gao ◽  
Jie Zhang ◽  
Hua Zhang ◽  
Lei Wang ◽  
Wanyi Gu
Keyword(s):  
Optical Networks ◽  
Transparent Optical Networks ◽  
End To End ◽  
Quality Of Transmission

Dynamic end-to-end optimization for quality of transmission in reconfigurable transparent optical networks

2008 ◽  
Vol 7 (6) ◽  
pp. 573 ◽  
Author(s):  
Hua Zhang ◽  
Jie Zhang ◽  
Lei Wang ◽  
Tao Liu ◽  
Wu Jia ◽  
...  
Keyword(s):  
Optical Networks ◽  
Transparent Optical Networks ◽  
End To End ◽  
Quality Of Transmission

scholarly journals AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

2020 ◽  
Vol 10 (1) ◽  
pp. 363 ◽  
Author(s):  
Xiaomin Liu ◽  
Huazhi Lun ◽  
Mengfan Fu ◽  
Yunyun Fan ◽  
Lilin Yi ◽  
...  
Keyword(s):  
Optical Networks ◽  
Performance Monitoring ◽  
Recent Progress ◽  
Optical Network ◽  
Data Driven ◽  
High Definition ◽  
Optical Performance Monitoring ◽  
Monitoring Tools ◽  
Quality Of Transmission

With the development of 5G technology, high definition video and internet of things, the capacity demand for optical networks has been increasing dramatically. To fulfill the capacity demand, low-margin optical network is attracting attentions. Therefore, planning tools with higher accuracy are needed and accurate models for quality of transmission (QoT) and impairments are the key elements to achieve this. Moreover, since the margin is low, maintaining the reliability of the optical network is also essential and optical performance monitoring (OPM) is desired. With OPM, controllers can adapt the configuration of the physical layer and detect anomalies. However, considering the heterogeneity of the modern optical network, it is difficult to build such accurate modeling and monitoring tools using traditional analytical methods. Fortunately, data-driven artificial intelligence (AI) provides a promising path. In this paper, we firstly discuss the requirements for adopting AI approaches in optical networks. Then, we review various recent progress of AI-based QoT/impairments modeling and monitoring schemes. We categorize these proposed methods by their functions and summarize advantages and challenges of adopting AI methods for these tasks. We discuss the problems remained for deploying AI-based methods to a practical system and present some possible directions for future investigation.

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