AdaPTwin: Adaptive Multi-Fidelity Predictive Digital Twin for Proactive Radio Resource Management in Vehicular Networks

TL;DR

AdaPTwin introduces an adaptive multi-fidelity digital twin framework that dynamically adjusts fidelity levels for proactive, latency-aware radio resource management in vehicular networks, significantly improving performance.

Contribution

It presents a novel adaptive multi-fidelity NDT that adjusts fidelity in real-time, integrating hierarchical architecture, advanced trajectory prediction, and efficient beamforming optimization.

Findings

Achieves up to 90% sum-rate gain over non-adaptive methods.

Reduces outage probability by 80% compared to non-adaptive NDTs.

Maintains real-time operation in dynamic vehicular scenarios.

Abstract

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.