Multi-agent learning for UAV networks: a unified approach to trajectory control, frequency allocation and routing
Year: 2025
Type: article
Abstract: Unmanned aerial vehicle (UAV) swarm networks have various applications. They support surveillance and communication. However, these networks face many challenges. High mobility causes frequent link breakages. Limited transmission range increases interference. Routing becomes complex due to these factors. This paper proposes a new approach. It jointly optimizes trajectory control, frequency allocation and routing. The proposed algorithm is called JTFR. It improves link utility in UAV swarm networks. Link utility depends on signal strength, queue delay and residual energy. The proposed JTFR uses multi-agent reinforcement learning. The learning model considers both local and neighbour information. This improves the stability of the network. A long short-term memory (LSTM) network is used. This helps in pre-dicting the best trajectory. A multi-head attention mechanism is also used. Each UAV adjusts its policy based on its neighbours. UAV swarm networks are dynamic. The movement of UAVs changes frequently. This affects network connectivity. The routing decisions must adapt to these changes. The proposed approach provides adaptive routing. JTFR is overall useful to improve UAV swarm communication. It handles trajectory control, frequency allocation and routing in balancing. The method is dynamic in the sense that it adapts to dynamic network conditions. The UAVs work well in complex environments.
DOI: 10.14419/474dfq89
Authors: Angara Satyam, R. Anil Kumar, Sarala Patchala, Sunitha Pachala, Geeta Bhimrao Atkar, U. S. B. K. Mahalaxm
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411211801