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

TL;DR

The study tackles connectivity in obstructed wireless networks by deploying an omnidirectional MRAV (o-MRAV) carrying a reconfigurable intelligent surface (RIS). It formulates a joint optimization problem to maximize the minimum user rate by optimizing the o-MRAV orientation $\boldsymbol{\Omega}$, 3D position $\mathbf{p}_R$, and RIS phase shifts $\boldsymbol{\Theta}$, using a $p$-norm smoothing of the min-rate objective and solving with Parallel Successive Convex Approximation (PSCA). The system employs MRT beamforming at the base station and assumes known channels, with FDMA to avoid interference. In simulations over a $1000\text{ m} \times 1000\text{ m}$ area, the proposed PLO scheme outperforms phase-and-position-only and phase-and-orientation schemes, achieving $28\%$ higher minimum rate and $14\%$ higher average rate, respectively, demonstrating the value of independent orientation control via o-MRAVs for aerial RIS-enhanced networks. Overall, the work shows that enabling independent orientation and position control of RIS-bearing UAVs significantly enhances network performance in obstructed environments.

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.

Figures (3)

• Figure 1: Communication Model
• Figure 2: (a) Minimum rate vs iterations, (b) Average rate vs iterations, (c) Optimized positions of o-MRAV.
• Figure 3: Optimal RIS orientation for PLO.