Harmonic Field-Based Global Guidance for Multi-Hop Routing in UAV Networks

As unmanned aerial vehicles (UAVs) increasingly operate in large-scale clusters, traditional routing protocols struggle to ensure efficient and time-sensitive packet path planning due to the growing network size and inherent mobility of UAVs. Meanwhile, despite deep learning (DL) based routing methods have shown promise in small UAV networks, their computational demands and limited scalability to large numbers of UAV nodes pose significant challenges. To address the challenges of scalability and computational demands in large-scale UAV networks, this paper proposes a novel decentralized global guided routing algorithm based on potential field. First, a potential field is constructed using a harmonic function to represent the current network status. Subsequently, leveraging this potential field, a global route is derived to define the overarching direction for data transmission. Finally, a compact neural network deployed at each node utilizes the global guidance direction and the local potential field information obtained from its surroundings to establish a specific data forwarding path within its maximum perception range. Simulation results illustrate the advantages of our proposed approach for establishing UAV paths in large-scale UAV networks.