Harnessing the Potential of Omnidirectional UAVs in RIS-Enabled Wireless Networks

2025-01-01

AI Summary

In RIS-assisted wireless networks, conventional underactuated multi-rotor aerial vehicles (u-MRAVs) suffer from coupled position and attitude dynamics, hindering joint optimization of RIS orientation and location and resulting in inadequate coverage in obstructed regions. Method: This paper proposes an omnidirectional multi-rotor aerial vehicle (o-MRAV), enabling the first joint optimization of 3D position, six-degree-of-freedom attitude, and RIS phase shifts. By decoupling RIS reflection direction control from air-interface channel design, we formulate a mixed-integer nonlinear program (MINLP) to maximize the minimum user rate, solved efficiently via objective function smoothing and a parallel successive convex approximation (PSCA) algorithm. Results: Experiments demonstrate that the proposed o-MRAV achieves a 28% gain in minimum rate and a 14% improvement in average rate over u-MRAV, significantly enhancing communication fairness and system capacity in obstructed environments.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Multirotor Aerial Vehicles (MRAVs) when integrated into wireless communication systems and equipped with a Reflective Intelligent Surface (RIS) enhance coverage and enable connectivity in obstructed areas. However, due to limited degrees of freedom (DoF), traditional under-actuated MRAVs with RIS are unable to control independently both the RIS orientation and their location, which significantly limits network performance. A new design, omnidirectional MRAV (o-MRAV), is introduced to address this issue. In this paper, an o-MRAV is deployed to assist a terrestrial base station in providing connectivity to obstructed users. Our objective is to maximize the minimum data rate among users by optimizing the o-MRAV's orientation, location, and RIS phase shift. To solve this challenging problem, we first smooth the objective function and then apply the Parallel Successive Convex Approximation (PSCA) technique to find efficient solutions. Our simulation results show significant improvements of 28% and 14% in terms of minimum and average data rates, respectively, for the o-MRAVs compared to traditional u-MRAVs.

Problem

• RIS-Enabled Wireless Networks
• Signal Coverage Optimization
• Unmanned Aerial Vehicle (UAV) Directionality

Innovation

• omnidirectional Micro-Rotor Aerial Vehicle
• intelligent reflecting surfaces
• wireless network coverage enhancement