# Joint Trajectory and Passive Beamforming Design for Intelligent Reflecting Surface-Aided UAV Communications: A Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2007.08380 · HTML: https://mlsanity.com/paper/2007.08380/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Deep progressive reinforcement learning-based flexible resource scheduling framework for IRS and UAV-assisted MEC system](https://mlsanity.com/paper/2408.01248/) (2024)
- [RIS-assisted UAV Communications for IoT with Wireless Power Transfer Using Deep Reinforcement Learning](https://mlsanity.com/paper/2108.02889/) (2021)
- [Joint User Association, Interference Cancellation and Power Control for Multi-IRS Assisted UAV Communications](https://mlsanity.com/paper/2312.04786/) (2023)
- [Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems](https://mlsanity.com/paper/2606.24416/) (2026)

## Abstract (arXiv)

In this paper, the intelligent reflecting surface (IRS)-aided unmanned aerial vehicle (UAV) communication system is studied, where the UAV is deployed to serve the user equipment (UE) with the assistance of multiple IRSs mounted on several buildings to enhance the communication quality between UAV and UE. We aim to maximize the energy efficiency of the system, including the data rate of UE and the energy consumption of UAV via jointly optimizing the UAV's trajectory and the phase shifts of reflecting elements of IRS, when the UE moves and the selection of IRSs is considered for the energy saving purpose. Since the system is complex and the environment is dynamic, it is challenging to derive low-complexity algorithms by using conventional optimization methods. To address this issue, we first propose a deep Q-network (DQN)-based algorithm by discretizing the trajectory, which has the advantage of training time. Furthermore, we propose a deep deterministic policy gradient (DDPG)-based algorithm to tackle the case with continuous trajectory for achieving better performance. The experimental results show that the proposed algorithms achieve considerable performance compared to other traditional solutions.