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The unmanned aerial vehicle (UAV)-enabled communication technology isregarded as an efficient and effective solution for some special applicationscenarios where existing terrestrial infrastructures are overloaded to providereliable services. To maximize the utility of the UAV-enabled system whilemeeting the QoS and energy constraints, the UAV needs to plan its trajectoryconsidering the dynamic characteristics of scenarios, which is formulated asthe Markov Decision Process (MDP). To solve the above problem, a deepreinforcement learning (DRL)-based scheme is proposed here, which predicts thetrend of the dynamic scenarios to provide a long-term view for the UAVtrajectory planning. Simulation results validate that our proposed schemeconverges more quickly and achieves the better performance in dynamicscenarios.

Technical Report for Trend Prediction Based Intelligent UAV Trajectory Planning for Large-scale Dynamic Scenarios