The Role of Digital Twin in Optical Communication: Fault Management, Hardware Configuration, and Transmission Simulation

Wang, Danshi; Zhang, Zhiguo; Zhang, Min; Fu, Meixia; Li, Jin; Cai, Shanyong; Zhang, Chunyu; Chen, Xue

doi:10.1109/mcom.001.2000727

Cited by 93 publications

(17 citation statements)

References 15 publications

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“…In [64], DITEN is applied to obtain the optimal communication resource allocation to alleviate the traffic congestion. In [65], the authors apply DT to connect the physical and network layer in the optical communication system, to improve the communication reliability and efficiency, and make the network management scheme better match the real physical situation. In [66], DT is employed to design an intelligent clock synchronization method in an industrial IoT scenario.…”

Section: Potentials Of Diten Towards 6gmentioning

confidence: 99%

Survey on Digital Twin Edge Networks (DITEN) Toward 6G

Tang

Chen

Rodrigues

et al. 2022

IEEE Open J. Commun. Soc.

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The next generation (6G) wireless systems aim to cater to the Internet of Everything (IoE) and revolutionize customer services and applications to a fully intelligent and autonomous system. To achieve this, the digital twin edge network (DITEN) is proposed to combine mobile/multi-access edge computing (MEC) and digital twin (DT), thereby improving the network performance such as throughput and security, and reducing the cost of communication, computation, and caching. In DITENs, the network status can be continuously monitored, and based on the obtained network states, the networking schemes, such as routing and resource management, can be studied in the established DITENs from a centralized perspective. In this survey, we present a comprehensive overview of DITEN for 6G. First, we present the fundamental aspects of DITEN, including concept, framework, and potential. Second, a comprehensive design of DITEN is devised, including the DT modeling/updating, DT deployment, key issues, and enabling technologies. Then, the typical applications of DITEN towards 6G are provided, including the Internet of Things (IoT), vehicular network, spaceair-groud integrated network (SAGIN), healthcare, wireless systems, and other applications, along with the design of DITEN in each application, such as DT modeling, DT association, incentive mechanisms, and so on. Finally, challenges and open issues are discussed.

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Section: Potentials Of Diten Towards 6gmentioning

confidence: 99%

Survey on Digital Twin Edge Networks (DITEN) Toward 6G

Tang

Chen

Rodrigues

et al. 2022

IEEE Open J. Commun. Soc.

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“…The interaction between reality and virtuality is realized by forwarding mapping to the virtual and backward reactions to physical objects. Virtual models will generate the corresponding target results according to the different requirements of various applications and then feedback the matching policies to the physical space [167].…”

Section: E Application Layermentioning

confidence: 99%

Internet of Intelligence: A Survey on the Enabling Technologies, Applications, and Challenges

Tang,

Yu,

Xie

et al. 2022

Preprint

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The Internet of intelligence is conceived as an emerging networking paradigm, which will make intelligence as easy to obtain as information. This paper provides an overview of the Internet of intelligence, focusing on motivations, architecture, enabling technologies, applications, and existing challenges. This can provide a good foundation for those who are interested to gain insights into the concept of the Internet of intelligence and the key enablers of this emerging networking paradigm. Specifically, this paper starts by investigating the evolution of networking paradigms and artificial intelligence (AI), based on which we present the motivations of the Internet of intelligence by demonstrating that networking needs intelligence and intelligence needs networking. We then present the layered architecture to characterize the Internet of intelligence systems and discuss the enabling technologies of each layer. Moreover, we discuss the critical applications and their integration with the Internet of intelligence paradigm. Finally, some technical challenges and open issues are summarized to fully exploit the benefits of the Internet of intelligence.

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“…In the context of optical communication, DRL is particularly useful for network control and automation and thus has been applied in the network layer to automatize the resolutions of routing, resource allocation, orchestration, and configuration (Chen et al, 2019a,b;Suárez-Varela et al, 2019;Andreoletti et al, 2020;Wang et al, 2021). A DRL-based routing solution was proposed for the optical transport network (OTN) that can better capture the crucial relationships among the lightpaths and paths in OTN topologies (Suárez-Varela et al, 2019).…”

Section: Deep Reinforcement Learning For Network Automationmentioning

confidence: 99%

Artificial Intelligence in Optical Communications: From Machine Learning to Deep Learning

Wang

Zhang

2021

Front. Comms. Net.

Self Cite

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Techniques from artificial intelligence have been widely applied in optical communication and networks, evolving from early machine learning (ML) to the recent deep learning (DL). This paper focuses on state-of-the-art DL algorithms and aims to highlight the contributions of DL to optical communications. Considering the characteristics of different DL algorithms and data types, we review multiple DL-enabled solutions to optical communication. First, a convolutional neural network (CNN) is used for image recognition and a recurrent neural network (RNN) is applied for sequential data analysis. A variety of functions can be achieved by the corresponding DL algorithms through processing the different image data and sequential data collected from optical communication. A data-driven channel modeling method is also proposed to replace the conventional block-based modeling method and improve the end-to-end learning performance. Additionally, a generative adversarial network (GAN) is introduced for data augmentation to expand the training dataset from rare experimental data. Finally, deep reinforcement learning (DRL) is applied to perform self-configuration and adaptive allocation for optical networks.

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The Role of Digital Twin in Optical Communication: Fault Management, Hardware Configuration, and Transmission Simulation

Cited by 93 publications

References 15 publications

Survey on Digital Twin Edge Networks (DITEN) Toward 6G

Survey on Digital Twin Edge Networks (DITEN) Toward 6G

Internet of Intelligence: A Survey on the Enabling Technologies, Applications, and Challenges

Artificial Intelligence in Optical Communications: From Machine Learning to Deep Learning

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