Adaptive and Reusable Digital Twins for Direct Satellite-to-Device Communication

The emergence of direct satellite-to-device (DS2D) communication technology promises transformative advancements in global connectivity, yet poses unprecedented challenges in network management. Digital twin (DT) technology, by creating highly accurate virtual models, aims to enhance DS2D networks by enabling real-time monitoring, predictive maintenance, and optimized resource allocation, moving beyond reactive responses to proactive management. However, current DT models, which rely on empirical models or manually constructed data-driven methods, lack the adaptability and reusability required to handle rapidly changing environmental conditions and the swift expansion of DS2D infrastructure. This limitation leads to frequent model reconstructions and significant resource wastage. To address these challenges, we propose an adaptive and reusable DT architecture tailored for DS2D networks. This architecture leverages modular design and model reuse to enhance adaptability, robustness, and maintainability. Furthermore, we introduce a contribution-based asynchronous federated meta-learning approach to enhance model reusability under dynamic connectivity and heterogeneous data conditions. Numerical results demonstrate the effectiveness of the proposed framework in enhancing the accuracy, generalization, and rapid adaptation of DT models.