Energy Efficient Aerial RIS: Phase Shift Optimization and Trajectory Design

TL;DR

This paper proposes an energy-efficient aerial RIS system with optimized phase shifts and drone trajectory design, demonstrating significant energy savings through a decoupled optimization approach and convex approximation techniques.

Contribution

It introduces a novel joint optimization framework for phase shifts and drone trajectory in aerial RIS systems, incorporating dynamic drone models and EMPC-based solutions.

Findings

Significant energy efficiency improvements shown in simulations.

Decoupled phase shift and trajectory optimization approach.

Convex approximation enables closed-form phase shift solutions.

Abstract

Reconfigurable Intelligent Surface (RIS) technology has gained significant attention due to its ability to enhance the performance of wireless communication systems. The main advantage of RIS is that it can be strategically placed in the environment to control wireless signals, enabling improvements in coverage, capacity, and energy efficiency. In this paper, we investigate a scenario in which a drone, equipped with a RIS, travels from an initial point to a target destination. In this scenario, the aerial RIS (ARIS) is deployed to establish a direct link between the base station and obstructed users. Our objective is to maximize the energy efficiency of the ARIS while taking into account its dynamic model including its velocity and acceleration along with the phase shift of the RIS. To this end, we formulate the energy efficiency problem under the constraints of the dynamic model of the drone. The studied problem is challenging to solve. To address this, we proceed as follows. First, we introduce an efficient solution that involves decoupling the phase shift optimization and the trajectory design. Specifically, the closed-form expression of the phase-shift is obtained using a convex approximation, which is subsequently integrated into the trajectory design problem. We then employ tools inspired by economic model predictive control (EMPC) to solve the resulting trajectory optimization. Our simulation results show a significant improvement in energy efficiency against the scenario where the dynamic model of the UAV is ignored.