Digital Twin – What Is It and How Can It Affect Future Networks

The concept of the Digital Twin (DT) has been around for years, but it has recently gained increased recognition. A Digital Twin is a software-based representation of a real-world system, device, or invention, designed to mimic its real-life counterpart as closely as possible. Essentially, it is a digital replica that users can interact with in a software environment. DTs are typically created using real-world data, allowing for the analysis and simulation of the system being replicated. By leveraging data collected from the physical world, we can validate the digital version and assess how accurately it mirrors the behavior of the original.

The approach and form of DT can vary depending on what is being replicated and the intended outcomes. A crucial factor is the level of fidelity between the real-world version and its digital counterpart, which is determined on a case-by-case basis. The guiding principle is to ensure the digital replica remains as true to reality as possible. The concept of a DT is also being explored and expanded in the context of real-world networks, such as mobile networks, under the emerging term Network Digital Twin (NDT).

In this blog post, we explore the concept of Digital Twin and its potential impact on future networks. We highlight the benefits of using NDTs and demonstrate how integrating Artificial Intelligence (AI) and Machine Learning (ML) with DT can enhance overall network performance. Additionally, we present the O-RAN ALLIANCE’s perspective on DT and discuss potential future developments in this area.

Why is Digital Twin used?

Once a system has a software representation, a world of possibilities opens up. The primary advantage is the ability to test solutions—such as using a Digital Twin in the automotive industry to simulate different driving scenarios—without risking any negative impact on the user experience (UX). This allows for parallel workflows, where one team can focus on maintaining the real-world system while another works with its digital counterpart. This flexibility enables us to modify the product without disrupting the actual system or device.

In the digital realm, we can simulate various real-life events to observe their potential effects on our solution. Furthermore, we can rerun the same scenario with different settings and compare the results. For example, we might simulate how a mobile network would handle an unusually high traffic volume. Once a potential solution is developed, we can implement changes, evaluate the outcomes, and identify possible improvements. The digital representation provides a dynamic platform where updates or changes can be easily tested and refined within the DT.

Digital Twin in 5G

There is growing interest in integrating 5G and beyond networks with their software counterparts—Network Digital Twins (NDTs) [1]. NDTs could play a critical role in developing these networks by enabling comprehensive testing of overall network behavior and ensuring that performance requirements are consistently met through continuous monitoring. Additionally, NDTs could enhance network security by detecting anomalies and immediately adjusting security configurations in response to potential attacks (for more on 5G/6G security, have a look here). However, this approach necessitates a connection between the real network and the NDT, which requires up-to-date, large datasets for accurate reproduction. This interface, while essential, could introduce vulnerabilities, underscoring the importance of securing it effectively. 

Another potential benefit of connecting a real 5G network with its digital twin is the use of AI for self-configuration. Data provided by the network could be transferred via an internal interface, simulated by the NDT, and then used to determine the optimal configuration for the real network. AI could manage both network optimization and security in this context. 

NDTs can operate in several operating modes [2]: 

• Retrospective Mode: NDT working retrospectively – collecting historical network data and analyzing it; 
• Introspective Mode: NDT working introspectively – generating real-time statistics from current network data; 
• Predictive Mode: NDT working predictively – forecasting future network behavior; 
• Proactive Mode: NDT working proactively – influencing the real network by preventing predicted errors. 

A key consideration here is the timescale, which can pose significant limitations. For instance, to predict network behavior in the next few minutes, the NDT would need to collect and analyze data from much longer periods (e.g., tens of minutes). The larger the dataset, the more accurate the predictions. However, processing this data quickly enough to make timely decisions is crucial, as it ensures that the network changes align with the NDT’s calculations. Decisions can be based on both short-term data and broader trends (e.g., daily or weekly).

When designing a DT, it’s important to strike a balance: A highly accurate DT may be computationally complex, time-consuming to simulate, and difficult to implement, while a less accurate DT is simpler and faster to calculate. In some cases, a highly accurate DT could be overkill, and it might be more practical to measure certain aspects directly within the real network.

Digital Twin within O-RAN

The architecture of an NDT comprises three main components: the Radio Access Network (RAN), the Digital Twin, and the interface connecting them [3], as illustrated in Figure 1. For seamless operation, there needs to be a standardized two-way interface that facilitates real-time communication, enabling the exchange of data for learning and allowing the DT to make and implement decisions based on the current network conditions. Within the O-RAN framework, there are tools available to create a DT for a network, which can communicate through standardized interfaces, such as O1 [4] and E2 [5], ensuring real-time network testing (for more information on O-RAN interfaces, click here). Integrating Machine Learning (ML) and Artificial Intelligence (AI) with NDT opens up new possibilities for enhancing operational efficiency, managing networks, and deploying innovative services.  

To this end, O-RAN ALLIANCE next Generation Research Group (O-RAN nGRG) has proposed four use cases [7] for integrating NDT within the O-RAN architecture:

• DT-RAN for AI/ML Training, Evaluation, and Performance Assurance;
• DT-RAN for Network Testing Automation;
• DT-RAN for Network Planning;
• DT-RAN for Network Energy Saving.

Given the complexity of 5G networks and the challenges associated with ensuring NDT accuracy, it may be necessary to develop DTs for specific parts of the network rather than for the entire network. The O-RAN nGRG has identified a research area focused on RF channel and antenna modeling technologies, referred to as DT-RF or DT-RAN-RF. This distinction is crucial due to the complexity of channel propagation, which requires higher accuracy to capture the behavior of this network segment. In the future, an NDT may consist of multiple DTs, each responsible for a specific part of the network. Together, these DTs would provide an accurate mapping of the real network.

With standardized DTs and interfaces between them, different companies could develop interoperable DTs that work seamlessly together. This would allow greater flexibility in selecting components to create an accurate NDT tailored to the specific network under consideration. The architecture required for the disaggregation of NDTs, including interoperability, aligns well with this vision thanks to its basic principles namely, openness and softwarization.

Conclusions

The Digital Twin (DT) concept is becoming increasingly significant in today’s world and is expected to play a crucial role in future networks. The development of NDTs could simplify the testing, development, and maintenance of the RAN. Moreover, when combined with AI, NDTs could enable intelligent automation in network operations, real-time traffic management, and proactive threat detection.

As networks grow more complex and adopt software-centric architectures, NDT will become an essential tool for network operators. It will not only enhance operational efficiency but also support rapid innovation and the deployment of new services, ensuring that future networks are flexible and capable of meeting the demands of the digital age.

Continuous testing of O-RAN components is crucial to ensure the effectiveness of O-RAN networks, including disaggregated, software-driven, and multi-vendor wireless systems. Additionally, planning network deployments and meeting the requirements of new generations are becoming increasingly challenging. Therefore, NDT will play a pivotal role in the future of O-RAN, as reflected in the recent work of the O-RAN nGRG.

References

[1] “Digital Twin for 5G and Beyond”, February 2021, IEEE Communications Magazine 59, Huan X Nguyen, Ramona Trestian, Duc To, Mallik Tatipamula
[2] M. Sanz Rodrigo, D. Rivera, J. I. Moreno, M. Àlvarez-Campana and D. R. López, „Digital Twins for 5G Networks: A Modeling and Deployment Methodology,” in IEEE Access, vol. 11, pp. 38112-38126, 2023
[3] “Network Digital Twin for Open RAN: The Key Enablers, Standardization, and Use Cases”, Javad Mirzaei, Ibrahim Abualhaol, Gwenael Poitau Advanced Wireless Technology, Dell Technologies Inc. arXiv:2308.02644v1 [cs.IT] 4 Aug 2023
[4] O-RAN ALLIANCE WG2, „A1 Interface: Type Definitions, v02.00”
[5] O-RAN ALLIANCE WG3, „E2 Application Protocol (E2AP), v2.03”
[6] “Colosseum: The Open RAN Digital Twin”, Michele Polese, Leonardo Bonati, Salvatore D’Oro, Pedram Johari, Davide Villa, Sakthivel Velumani, Rajeev Gangula, Maria Tsampazi, Clifton Paul Robinson, Gabriele Gemmi, Andrea Lacava, Stefano Maxenti, Hai Cheng, Tommaso Melodia, arXiv:2404.17317v1 [cs.NI] 26 Apr 2024
[7] O-RAN nGRG, „Research Report on Digital Twin RAN Use Cases”, July 2024, [available online]

Other Resources from Rimedo Labs

• To check our blog posts on #AllThingsWireless, visit: Blog – RIMEDO Labs
• Webinar: A video recording from an O-RAN Webinar: O-RAN Architecture and Use Cases – YouTube
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• The O-RAN Whitepaper 2023: Security in O-RAN : Subscribe to get your O-RAN Whitepaper copy.
• The O-RAN Whitepaper 2024: Traffic Steering in O-RAN: Subscribe to get your O-RAN Whitepaper copy.

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