FALCON: Fanet-Aware Learning and digital twin CONtrol framework

The rapid evolution of telecommunication networks is leading to increasingly complex systems, requiring adaptive, flexible, and intelligent mechanisms for resource management, orchestration, and access control. In this context, the Network Digital Twin (NDT) paradigm emerges as a powerful tool to model the behavior of devices, communication links, operating environments, and applications in complex networks. This paper introduces FALCON, a Digital-Twin-based orchestration framework designed to optimize horizontal offloading in UAV-based Flying Ad Hoc Networks (FANETs) providing edge computing services to ground devices in remote areas. FALCON integrates multiple Smart Agents (DQN, A2C, PPO) running concurrently on the Digital Twin to dynamically determine the optimal offloading probabilities. A proof-of-concept demonstrates how the framework performs real-time What-if Scenario analyses and adapts to varying workload and channel conditions. Numerical results highlight the gains achieved through coordinated model selection and reuse, showing reduced end-to-end delay and faster convergence compared to standalone DRL-based controllers. • Digital Twin framework for real-time FANET orchestration. • Parallel Smart Agents enable fast What-if scenario evaluation. • Dynamic model selection adapts to FANET state and intents. • Reduced service delay compared to standalone DRL methods. • Model reuse ensures rapid reaction to changing UAV conditions.