Performance Evaluation Methodology for Federated XR Network Digital Twins in AI-Aware 6G Networks

Network digital twins (NDTs) are emerging as key enablers of 6G networks integrating artificial Intelligence (AI) techniques. NDT systems offer novel features, including real-time monitoring, simulation, enhanced network planning, autonomous management, seamless integration with emerging technologies such as extended reality (XR), among others. When NDTs converge with XR, NDTs can be customized with additional interactive services that are not available in the original network. In this work, artificial intelligence (AI) strategies are applied to a set of XR functions within federated NDTs. While existing NDT approaches follow a one-to-one (1:1) model, where a single NDT instance is deployed from an original network, the one-to-many (1:N) federation model requires the orchestration of multiple XR-tailored NDT instances. The federation of NDTs can be applied across diverse 6G use cases, including telemedicine, UAV management, Industry 4.0, and the remote driving of complex vehicles. Ensuring the optimal operation of the NDT federation requires a methodology tailored to the requirements of each use case. This paper introduces a score-based performance analysis to quantify the benefits achieved through NDT federation. Unlike existing models for the digital twin (DT) federation, this paper introduces a KPI-based rational model that quantifies the trade-off between federation benefits and the associated operational complexity. A mathematical analysis is performed to validate the consistency of the score formula both in general terms and within the context of each specific use case.