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

TL;DR

This paper proposes a deep reinforcement learning-based method for UAV trajectory planning in large-scale dynamic scenarios, predicting environmental trends to optimize long-term UAV performance under QoS and energy constraints.

Contribution

It introduces a novel DRL scheme that predicts scenario trends for improved UAV trajectory planning in dynamic environments, enhancing convergence speed and performance.

Findings

Faster convergence of the proposed scheme.

Better performance in dynamic scenarios.

Effective long-term trajectory planning.

Abstract

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.