Energy-Efficient UAV-Mounted RIS Assisted Mobile Edge Computing

TL;DR

This paper proposes a novel UAV-mounted RIS assisted MEC system that optimizes UAV trajectory, RIS beamforming, and resource allocation to significantly enhance energy efficiency, addressing practical limitations of UAV and RIS in mobile edge computing.

Contribution

It introduces a joint optimization framework for UAV trajectory, RIS beamforming, and MEC resource allocation in a U-RIS system, providing a high-performance suboptimal solution.

Findings

Significant improvement in energy efficiency demonstrated through simulations.

Effective iterative algorithm based on SCA and Dinkelbach method.

Addresses practical limitations of UAV and RIS in MEC environments.

Abstract

Unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) have been recently applied in the field of mobile edge computing (MEC) to improve the data exchange environment by proactively changing the wireless channels through maneuverable location deployment and intelligent signals reflection, respectively. Nevertheless, they may suffer from inherent limitations in practical scenarios. UAV-mounted RIS (U-RIS), as a promising integrated approach, can combine the advantages of UAV and RIS to break the limit. Inspired by this, we consider a novel U-RIS assisted MEC system, where a U-RIS is deployed to assist the communication between the ground users and an MEC server. The joint UAV trajectory, RIS passive beamforming and MEC resource allocation design is developed to maximize the energy efficiency (EE) of the system. To tackle the intractable non-convex problem, we divide it into two subproblems and solve them iteratively based on successive convex approximation (SCA) and the Dinkelbach method. Finally we obtain a high-performance suboptimal solution. Simulation results show that the proposed algorithm significantly improves the energy efficiency of the MEC system.