Multi-agent learning for UAV networks: a unified approach to trajectory control, frequency allocation and routing

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.