Trajectory Planning of UAV-Enabled Data Uploading for Large-Scale Dynamic Networks: A Trend Prediction Based Learning Approach

The unmanned aerial vehicle (UAV) enabled communication technology is regarded as an efficient and effective solution to provide emergency data uploading for some special cases where existing cellular infrastructures cannot provide reliable services to large-scale ground users (GUs). In the face of large-scale dynamic networks where the number and location of GUs change all the time, to maximize the throughput of data upload tasks under the premise of meeting the requirements of fairness, QoS and energy consumption, we propose a UAV trajectory planning approach based on deep reinforcement learning in combination with the prediction of GUs' movement trend in future time slots. Simulation results validate that our proposed approach converges more quickly and achieves better performance in dynamic networks.