Three-Dimension Trajectory Design for Multi-UAV Wireless Network With Deep Reinforcement Learning

The effective trajectory design of multiple unmanned aerial vehicles (UAVs) is investigated for improving the capacity of the communication system. The aim is for maximizing real-time downlink capacity under the coverage constraint by reaping the mobility benefits of UAVs. The problem of three-dimension (3D) dynamic movement of UAVs under coverage constraint is formulated as a Constrained Markov Decision Process (CMDP) problem, while a constrained Deep Q-Network (cDQN) algorithm is proposed for solving the formulated problem. In the proposed cDQN model, each UAV acts as an agent to explore and learn its 3D deploying policy. The aim of the proposed cDQN model is for obtaining the maximum capacity while attempting to guarantee that all ground terminals (GTs) are covered. In order to satisfy the coverage constraint, a primal-dual method is adopted for training primal variable and dual variable (lagrangian multiplier) in turn. Furthermore, in an effort to reduce the action space of the cDQN algorithm, prior information is utilized for eliminating the invalid actions by the action filter. Experiment results demonstrate that the cDQN algorithm is capable of converging after some training steps. Additionally, the UAVs are capable of adapting the movement of GTs under the coverage constraint according to the 3D deploying policy derived from the proposed cDQN algorithm.

Automated summary

The study investigates the effective trajectory design of multiple UAVs to enhance communication system capacity, utilizing a Deep Q Network algorithm to maximize real-time downlink capacity under coverage constraints while ensuring all ground terminals are covered.