The highly dynamic nature of vehicular networks necessitates proactive and site-specific radio resource management (RRM) to achieve ultra-reliable low-latency communications. While Network Digital Twins (NDTs) have emerged as a promising enabler, ray-tracing remains time-consuming, challenging accurate RRM under latency constraints. We propose AdaPTwin, an adaptive multi-fidelity predictive NDT for proactive and latency-aware RRM in vehicular networks. Unlike single- and multi-fidelity NDTs with fixed fidelity levels, AdaPTwin dynamically adjusts NDT fidelity based on network conditions. The framework adopts a hierarchical cloud-edge architecture, where computationally intensive fidelity selection is performed periodically in the cloud, and the proactive RRM loop operates in real-time at the edge. The edge-based proactive RRM task consists of channel prediction between vehicles and roadside units (RSUs) via trajectory forecasting and look-ahead ray tracing, followed by RRM execution. A transformer model enhanced with continual and transfer learning enables vehicular trajectory prediction while adapting to new environments and traffic patterns. Ray-tracing is performed using NVIDIA Sionna by exploiting a dynamically updated virtual environment to ensure realistic radio propagation within the NDT. Furthermore, a joint RSU beamforming and vehicle-RSU association problem is formulated to maximize proportionally fair sum-rate, and it is efficiently solved using a scalable multi-start iterative coordinate descent algorithm. Comparisons against reactive, single-fidelity, and non-adaptive predictive NDTs under realistic vehicular conditions confirm that AdaPTwin successfully adapts to diverse scenarios where other frameworks fail. Ultimately, AdaPTwin achieves up to 90% sum-rate gain and 80% outage probability reduction compared to non-adaptive NDTs, while maintaining real-time performance.

翻译：车辆网络的高度动态特性要求采取主动且场地特定的无线资源管理（RRM），以实现超可靠低延迟通信。尽管网络数字孪生（NDT）已成为一种有前景的使能技术，但光线追踪仍耗时较长，在延迟约束下难以实现精确的RRM。我们提出AdaPTwin——一种面向车辆网络中主动式且感知延迟的RRM的自适应多保真度预测型NDT。与采用固定保真度级别的单保真度及多保真度NDT不同，AdaPTwin根据网络条件动态调整NDT保真度。该框架采用分层云-边缘架构，其中计算密集型的保真度选择在云端周期性执行，而主动式RRM循环则在边缘端实时运行。基于边缘的主动式RRM任务包括：通过轨迹预测和前向光线追踪对车辆与路侧单元（RSU）间的信道进行预测，随后执行RRM。一种结合持续学习与迁移学习的Transformer模型能够实现车辆轨迹预测，同时适应新环境与交通模式。光线追踪利用NVIDIA Sionna基于动态更新的虚拟环境执行，以确保NDT内无线传播的逼真性。此外，我们构建了一个联合RSU波束赋形与车辆-RSU关联的优化问题以最大化比例公平和速率，并通过一种可扩展的多起点迭代坐标下降算法高效求解。在真实车辆条件下与反应式、单保真度及非自适应预测型NDT的对比验证表明，AdaPTwin成功适应了其他框架失效的多样化场景。最终，与非自适应NDT相比，AdaPTwin在保持实时性能的同时，实现了高达90%的和速率增益及80%的中断概率降低。