Time-Effective UAV-IRS-Collaborative Data Harvesting: A Robust Deep Reinforcement Learning Approach

The collaboration between unmanned aerial vehicles (UAVs) and intelligent reflecting surfaces (IRSs) presents an innovative approach for delay-tolerant data harvesting in distributed Internet of Things (IoT) networks. However, existing research mostly overlooks the dynamic changes in communication links caused by the real-time UAV movement and the realistic geographical features. In this paper, we address these challenges by considering a practical three-dimensional (3D) urban scenario with a centralized IRS. Our aim is to minimize the completion time of data harvesting missions by jointly optimizing the 3D trajectory of the UAV and the phase shift of the IRS. Specifically, the formulated problem is decoupled into two subproblems. First, for the 3D continuous trajectory design, we propose a robust memory-based softmax deep double deterministic policy gradients (MSD3) approach, which enables the UAV to adaptively collect delay-tolerant data from randomly distributed ground devices starting from any arbitrary point. Second, we present a comprehensive theoretical analysis for the continuous IRS phase control, which provides a practical and intuitive numerical solution. Simulation results demonstrate that the proposed MSD3-IRS algorithm outperforms other mainstream baselines based on deep reinforcement learning.