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Digital Twin assisted AI for sustainable Radio Access Networks

Telecommunications / Christian Doppler Laboratory / Digital Twin assisted AI for sustainable Radio Access Networks

This CD laboratory aims to create a foundation for using artificial intelligence (AI) based learning and training methods in wireless networks in various scenarios, with the benefits of efficiency, sustainability, and reliability. For this purpose, we develop so-called “digital twins” (DT), representing enormously different environments such as trains, industrial sites, and dynamic environments, along with the corresponding wireless access and user populations.

Photo Digital Train Twin Open Image Gallery
Digital Train Twin
Photo Project Overview Open Image Gallery
Digital Twin Assisted AI for sustainable RAN

Enhancing Mobile Networks for Diverse Applications: The Role of 5G and Digital Twins

In today’s world, mobile networks are not just a convenience; they’re a necessity for both daily life and the increasingly digital industrial sector. However, the demands placed on these networks vary widely based on users, environments, and applications, presenting a unique set of challenges.

The Diverse Demands of Mobile Network Users

Consider the varied scenarios where mobile networks are critical: A train passenger wishes to work, make phone calls, surf the internet, or enjoy digital entertainment during their journey. Here, the network must overcome physical barriers like the train’s design and environmental factors like rural areas with poor coverage to provide seamless service. In contrast, industrial applications demand unparalleled reliability for real-time machine networking, pushing the network to function effectively even under the most demanding conditions.

The Evolution of Mobile Networks: Embracing 5G

The advent of 5G technology marks a significant leap forward, introducing the ability for dynamic resource optimization and network slicing to meet these diverse requirements. This technological evolution allows the network to adapt in real-time, offering tailored solutions for both the commuter streaming a video on a train and the factory relying on precise machine-to-machine communication.

The CD Laboratory’s Innovative Approach

Leading the charge in this field is Philipp Svoboda and his team at the CD laboratory, who are developing a model-based agent for automated network optimization. This system intelligently assesses current conditions and predicts future demands to dynamically allocate resources, ensuring optimal network performance at all times.

Digital Twins: A Game-Changer for Network Management

Central to their approach is the use of digital twins—virtual replicas of physical systems—that enable accurate simulations and predictions. These digital twins are instrumental in refining AI-driven network management, offering a way to visualize and manipulate the network’s response to various scenarios without the need for extensive real-world testing.

Towards a Resource-Efficient Future

Traditionally, machine learning (ML) applications required vast amounts of data, making them resource-intensive. However, by creating digital twins of railway networks, industrial environments, and other dynamic settings, the team can employ ML more efficiently. This innovative use of digital twins not only enhances network reliability and performance but also paves the way for more sustainable and cost-effective solutions.

As we move forward, the work being done in this CD laboratory exemplifies the potential of 5G and digital twins to revolutionize mobile networks, catering to the complex and varied needs of modern users and industries alike.

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