Call for Papers

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As the internet evolves, communication networks have become essential infrastructure for both society and industry. To meet the increasingly stringent requirements of emerging network applications (e.g., metaverse) and distributed computing systems (e.g., high-performance storage), modern networks grow larger and more heterogeneous, resulting in highly dynamic and unpredictable network behavior. As a result, modern communication networks have become very complex and costly to manage, operate, and optimize. Digital twin paradigm has been adopted recently by the manufacturing industry to characterize complex and dynamic systems (e.g., smart city, engine design). A digital twin can be treated as a digital representation of a physical object or system, which run alongside real-time processes and provide a linkage between the physical and digital worlds. The main advantage of a digital twin is that it can accurately model a complex system without interacting with it, which would otherwise be costly in the physical world.

Network digital twin aims at providing a virtual representation of a physical network system that is used to simulate various design scenarios, validate policies, and assess the behavior of the network system. In this context, a network digital twin can be a key enabler of effective control and management of modern communication networks. The use of network digital twin allows network operators to perform network architecture/protocol design, network planning, troubleshooting, network optimization, and network upgrading under a variety of “what-if” scenarios. These operations can be executed in real-time, without jeopardizing the physical network because the interaction with the digital twin does not require access to the real-world network system. To make it feasible, various challenges need to be resolved, which span network simulation and modeling, network monitoring and measurements, network verification and optimization, etc.

In this Special Issue (SI), we aim to present and showcase the latest advances with the shared scope of the theories, methods, implementations, and applications of network digital twin.

This SI is targeted at the above-indicated issues related to network digital twin. Authors are invited to submit previously unpublished papers. Topics include, but are not limited to:

• Self-driving network and network intelligence.
• Intent-based networking.
• Network performance modeling.
• Data-driven approaches for network intelligence or network digital twin.
• Artificial intelligence and machine learning for network digital twin.
• Network simulation or emulation.
• Network monitoring and measurement.
• Network verification and optimization with network digital twin.
• Performance evaluation with network digital twin.
• Testbed and test method for network protocols and algorithms.
• Network data collection, representation, and datasets.
• Network traffic modeling and analysis.
• Network security with network digital twin.
• Visualization for network digital twin.
• Programmable network for network digital twin.
• Implementation and evaluation of network digital twin.
• Standardization for network digital twin.

Submission Guidelines

Manuscripts should conform to the standard format as indicated in the “Information for Authors” section of the Paper Submission Guidelines. All manuscripts to be considered for publication must be submitted by the deadline through Manuscript Central. Select “January 2024 / NetworkDigitalTwin” from the drop-down menu of Topic titles.

Important Dates

Manuscript Submission Deadline: 15 July 2023
Initial Decision Notification: 15 September 2023
Revised Manuscript Due: 15 October 2023
Final Decision Notification: 1 November 2023
Final Manuscript Due: 20 November 2023
Publication Date: January/February 2024

Guest Editors

Yong Cui
Tsinghua University, China

Jiangchuan Liu
Simon Fraser University, Canada

Minlan Yu
Harvard University, USA

Junchen Jiang
The University of Chicago, USA

Liang Zhang
Huawei AI4Net Lab, China

Lu Lu
China Mobile Research Institute, China