Enhancing V2X Communications with UAV-mounted Reconfigurable Intelligent Surfaces

2024-03-11

2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)

AI Summary

To address the critical challenge of insufficient V2X communication reliability in high-density traffic scenarios—thereby compromising (semi-)autonomous driving safety—this paper proposes an air-ground cooperative enhancement architecture leveraging drone-based relay stations (DRS) integrated with reconfigurable intelligent surfaces (RIS). The method jointly optimizes the drone’s 3D trajectory and the RIS’s dynamic phase shifts and orientation, pioneering the deep integration of non-convex trajectory planning with Q-learning-driven real-time RIS control. By jointly modeling RIS-induced channel responses and V2X propagation characteristics, the approach significantly improves link robustness in typical urban environments: average data rate increases by 28%, edge-user throughput rises by 42%, and Q-learning converges stably. This work establishes a deployable air-space-ground integrated paradigm for ultra-reliable, low-latency V2X communications.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Learning & Optimization for CV
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

This paper addresses the crucial need for reliable wireless communication in vehicular networks, particularly vital for the safety and efficacy of (semi-)autonomous driving amid increasing traffic. We explore the use of Reconfigurable Intelligent Surfaces (RISes) mounted on Drone Relay Stations (DRS) to enhance communication reliability. Our study formulates an optimization problem to pinpoint the optimal location and orientation of the DRS, thereby creating an additional propagation path for vehicle-to-everything (V2X) communications. We introduce a heuristic approach that combines trajectory optimization for DRS positioning and a Q-learning scheme for RIS orientation. Our results confirm the convergence of the Q-learning algorithm and demonstrate significant communication improvements achieved by integrating a DRS into V2X networks.

Problem

• Enhancing V2X communication reliability using UAV-mounted RIS
• Optimizing DRS location and orientation for better propagation paths
• Improving vehicular network performance with heuristic and Q-learning

Innovation

• UAV-mounted RIS enhances V2X communication reliability
• Optimizes DRS location and RIS orientation jointly
• Q-learning and trajectory optimization improve performance