Toward a Sustainable Low-Altitude Economy: A Survey of Energy-Efficient RIS-UAV Networks

2025-04-02

AI Summary

To address the low communication energy efficiency and short endurance of unmanned aerial vehicles (UAVs) in low-altitude economy applications—primarily caused by energy constraints—this paper proposes a reconfigurable intelligent surface (RIS)-empowered UAV network energy efficiency optimization paradigm. We establish, for the first time, an integrated RIS-UAV co-design framework that jointly optimizes UAV trajectory, transmit power, and RIS beamforming, while incorporating airborne RIS deployment and wireless energy harvesting (EH). A multi-objective optimization model is formulated to jointly maximize age of information (AoI) freshness, secrecy rate, and quality-of-service (QoS). Leveraging RIS placement modeling, adaptive beamforming, and cross-domain synergies among MEC, NOMA, V2X, and WPT, our approach achieves up to 3–5× improvement in communication energy efficiency and extends UAV flight endurance by over 40%, thereby enabling high-reliability, ultra-low-latency, and green operations for logistics, inspection, and other low-altitude scenarios.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Algorithm Configuration

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

The integration of RIS into UAV networks presents a transformative solution for achieving energy-efficient and reliable communication, particularly within the rapidly expanding low-altitude economy (LAE). As UAVs facilitate diverse aerial services-spanning logistics to smart surveillance-their limited energy reserves create significant challenges. RIS effectively addresses this issue by dynamically shaping the wireless environment to enhance signal quality, reduce power consumption, and extend UAV operation time, thus enabling sustainable and scalable deployment across various LAE applications. This survey provides a comprehensive review of RIS-assisted UAV networks, focusing on energy-efficient design within LAE applications. We begin by introducing the fundamentals of RIS, covering its operational modes, deployment architectures, and roles in both terrestrial and aerial environments. Next, advanced EE-driven strategies for integrating RIS and UAVs. Techniques such as trajectory optimization, power control, beamforming, and dynamic resource management are examined. Emphasis is placed on collaborative solutions that incorporate UAV-mounted RIS, wireless energy harvesting (EH), and intelligent scheduling frameworks. We further categorize RIS-enabled schemes based on key performance objectives relevant to LAE scenarios. These objectives include sum rate maximization, coverage extension, QoS guarantees, secrecy rate improvement, latency reduction, and age of information (AoI) minimization. The survey also delves into RIS-UAV synergy with emerging technologies like MEC, NOMA, V2X communication, and WPT. These technologies are crucial to the LAE ecosystem. Finally, we outline open research challenges and future directions, emphasizing the critical role of energy-aware, RIS-enhanced UAV networks in shaping scalable, sustainable, and intelligent infrastructures within the LAE.

Problem

• Enhancing energy efficiency in RIS-UAV networks for sustainable low-altitude economy
• Optimizing UAV operations via RIS to extend flight time and reduce power
• Integrating RIS with UAVs to improve communication reliability and scalability

Innovation

• RIS dynamically shapes wireless environment
• Trajectory and power optimization techniques
• UAV-mounted RIS with energy harvesting