arXiv:2507.07067v4 Announce Type: replace-cross Abstract: Training effective artificial intelligence models for telecommunications is challenging due to the scarcity of deployment-specific data. Real data collection is expensive, and available datasets often fail to capture the unique operational conditions and contextual variability of the network environment. Digital twinning provides a potential solution to this problem, as simulators tailored to the current network deployment can generate site-specific data to augment the available training datasets. However, there is a need to develop sol
The increasing complexity of telecommunications networks and the demand for AI-driven optimization are pushing the need for more efficient and accurate model training data.
Bridging the sim-to-real gap allows for the practical application of AI in real-world network deployments, improving efficiency, resilience, and potentially enabling new functionalities.
The ability to generate high-fidelity, deployment-specific data through digital twins would significantly reduce the cost and time associated with training robust AI models for telecommunications.
- · Telecommunication companies
- · AI/ML model developers
- · Digital twin platform providers
- · Network equipment manufacturers
- · Companies reliant on solely real-world data collection
- · Less agile network operators
Improved performance and reliability of AI-managed telecommunication networks.
Faster deployment of new AI-driven network features and services due to reduced training time and cost.
Enhanced resilience of critical infrastructure against evolving threats, and potentially new revenue streams through optimized network resource allocation.
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