A Robust Deep Q-Network (DQN) for Heterogeneous Tasks and QoS-Aware UAV Relay Communication Optimization

Unmanned aerial vehicles (UAVs) play a significant role in wireless communication because of their high maneuverability and the advantage of forming Line-of-Sight (LoS) links with ground users. In this research, we investigate the trajectory design and resource scheduling problem of UAV relay communication optimization scenarios, considering heterogeneous tasks and quality of service (QoS) awareness. First, we model the optimization scenario and transform the problem-solving into a markov decision process (MDP). Next, we propose R-DQN, a robust deep Q-network (DQN) algorithm tailored for heterogeneous tasks and QoS-aware UAV relay communication optimization scenarios. R-DQN introduces corresponding mechanisms in the training process, network structure, and sampling method to improve the effective exploration capability of DQN, making it more robust and suitable for the dynamic constrained optimization scenario tackled. Simulations and experimental results show that the proposed R-DQN algorithm has better convergence and global optimization abilities than other algorithms, such as Dueling DQN, Noisy DQN, and DDQN.