The Future of Aerial Communications: A Survey of IRS-Enhanced UAV Communication Technologies
Not on the map. Why?
Abstract (arXiv)
The advent of Intelligent Reflecting Surfaces (IRS) and Unmanned Aerial Vehicles (UAVs) is setting a new benchmark in the field of wireless communications. IRS, with their groundbreaking ability to manipulate electromagnetic waves, have opened avenues for substantial enhancements in signal quality, network efficiency, and spectral usage. These surfaces dynamically reconfigure the propagation environment, leading to optimized signal paths and reduced interference. Concurrently, UAVs have emerged as dynamic, versatile elements within communication networks, offering high mobility and the ability to access and enhance coverage in areas where traditional, fixed infrastructure falls short. This paper presents a comprehensive survey on the synergistic integration of IRS and UAVs in wireless networks, highlighting how this innovative combination substantially boosts network performance, particularly in terms of security, energy efficiency, and reliability. The versatility of UAVs, combined with the signal-manipulating prowess of IRS, creates a potent solution for overcoming the limitations of conventional communication setups, especially in challenging and underserved environments. Furthermore, the survey delves into the cutting-edge realm of Machine Learning (ML), exploring its role in the strategic deployment and operational optimization of UAVs equipped with IRS. The paper also underscores the latest research and practical advancements in this field, providing insights into real-world applications and experimental setups. It concludes by discussing the future prospects and potential directions for this emerging technology, positioning the IRS-UAV integration as a transformative force in the landscape of next-generation wireless
Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 7 of its 42 references that are in the map. First posted on arXiv 2 Jun 2024.
Builds on
7 of 42 references.
References · 7
- 2020Machine Learning Empowered Trajectory and Passive Beamforming Design in UAV-RIS Wireless Networkscited by 7
- 2019UAV-to-UAV Communications in Cellular Networkscited by 6
- 2020Optimizing Age of Information Through Aerial Reconfigurable Intelligent Surfaces: A Deep Reinforcement Learning Approachcited by 6
- 2020Robust Secure UAV Communications with the Aid of Reconfigurable Intelligent Surfacescited by 5
- 2021RIS-Assisted UAV for Timely Data Collection in IoT Networkscited by 3
- 2021Resource Management for Transmit Power Minimization in UAV-Assisted RIS HetNets Supported by Dual Connectivitycited by 3
- 2022Active-Passive IRS aided Wireless Communication: New Hybrid Architecture and Elements Allocation Optimizationcited by 1
Built on by
Not cited yet.