  The unmanned aerial vehicle (UAV)-enabled communication technology is
regarded as an efficient and effective solution for some special application
scenarios where existing terrestrial infrastructures are overloaded to provide
reliable services. To maximize the utility of the UAV-enabled system while
meeting the QoS and energy constraints, the UAV needs to plan its trajectory
considering the dynamic characteristics of scenarios, which is formulated as
the Markov Decision Process (MDP). To solve the above problem, a deep
reinforcement learning (DRL)-based scheme is proposed here, which predicts the
trend of the dynamic scenarios to provide a long-term view for the UAV
trajectory planning. Simulation results validate that our proposed scheme
converges more quickly and achieves the better performance in dynamic
scenarios.
