Graph-Density-Aware Joint Energy-Latency Optimization in Multi-UAV IoT Networks Using Dueling Deep Q-Network

Multi-UAV communication networks face significant challenges in achieving high energy efficiency and low communication latency under dynamic topology and interference conditions. This paper proposes a Dueling Deep Q-Network (DQN) framework for joint resource optimization in 6G-enabled multi-UAV systems. The proposed approach jointly optimizes transmit power allocation, inter-UAV link association, and adaptive graph density within a unified reinforcement learning framework. By employing a dueling value–advantage decomposition, the proposed model improves learning stability and convergence compared to conventional DQN and Double DQN (DDQN) schemes. Simulation results under varying network densities and UAV scales show that the proposed Dueling DQN achieves up to 15% higher energy efficiency and 12% lower end-to-end latency, while maintaining robust performance in dense connectivity scenarios. These results demonstrate the effectiveness and scalability of the proposed framework for energy- and latency-sensitive UAV communication applications.