Joint Resource Scheduling of the Time Slot, Power, and Main Lobe Direction in Directional UAV Ad Hoc Networks: A Multi-Agent Deep Reinforcement Learning Approach
Directional unmanned aerial vehicle (UAV) ad hoc networks (DUANETs) are widely applied due to their high flexibility, strong anti-interference capability, and high transmission rates.However, within directional networks, complex mutual interference persists, necessitating scheduling of the time slot, power, and main lobe direction for all links to