JOINT POWER CONTROL AND SPECTRUM ACCESS OPTIMIZATION IN FANET VIA REINFORCEMENT LEARNING

Abstract

This paper studies joint power control and spectrum access in dynamic flying ad hoc networks (FANETs) under co-channel interference and adaptive jamming. We propose a graph neural network-aided multi-agent reinforcement learning (GNN- MARL) framework that enables distributed, topology-aware decision-making. Each UAV exploits local observations while leveraging graph embeddings to capture time-varying network structure. Simulation results show that the proposed approach improves average throughput by 25-35% compared to DQN- based baselines, while increasing the probability of satisfying QoS constraints by approximately 20%. The method also enhances energy efficiency and maintains fairness among UAVs. Under high mobility and adaptive jamming, performance degradation is limited to within 10%, demonstrating strong robustness. Ablation analysis indicates that removing the GNN module reduces performance by up to 18%, and single-agent variants incur an additional 20-25% loss, highlighting the importance of topology-aware multi- agent learning. Moreover, the framework scales effectively with network size, exhibiting only 5-8% variation in performance. These results confirm that the proposed GNN- MARL framework provides a scalable and robust solution for resource optimization in highly dynamic and adversarial FANET environments. Keywords: FANET; Multi-agent reinforcement learning; Graph neural networks; Anti-jamming; Resource optimization.