Method of Minimizing Energy Consumption for RIS Assisted UAV Mobile Edge Computing System

In urban settings, the highly complex communication environment leads to a frequent occurrence blockage in the link between ground users and unmanned aerial vehicle (UAV), which renders the quality of communication poor. To address this issue, an UAV edge computing system based on reconfigurable int...

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Bibliographic Details
Published in:IEEE access 2024-01, Vol.12, p.1-1
Main Authors: Zhuo, Zhihai, Dong, Shuo, Zheng, Hui, Zhang, Yuexia
Format: Article
Language:English
Subjects:
Autonomous aerial vehicles
Edge computing
Energy consumption
Energy management
Evolution & development
Evolutionary algorithms
Ground support
Kuhn-Tucker method
Lagrangian functions
Lyapunov methods
Mobile computing
mobile edge computing
Multi-access edge computing
Optimization
Queues
reconfigurable intelligent surface
Reconfigurable intelligent surfaces
Resource allocation
Resource management
Simulation
Stability
Stability analysis
Trajectory optimization
Unmanned aerial vehicle (UAV) communication
Unmanned aerial vehicles
Urban areas
Urban environments
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Summary:In urban settings, the highly complex communication environment leads to a frequent occurrence blockage in the link between ground users and unmanned aerial vehicle (UAV), which renders the quality of communication poor. To address this issue, an UAV edge computing system based on reconfigurable intelligent surface (RIS) is proposed in this paper. The goal of this system is to minimize energy consumption by considering various factors such as user transmission power, intelligent reflector phase shift matrix, UAV trajectory, computational resource allocation and the stability of task queues. By applying the Lyapunov optimization theory, the original non-convex problem is decomposed into a series of optimization-related sub-problems. Additionally, it is possible to obtain the closed solution to the original problem through Lagrange dual, Karush-Kuhn-Tucker (KKT) conditions, and mixed differential genetic evolution algorithm (MDGEA). As indicated by the simulation results, the proposed RIS-assisted UAV edge computing system is more effective in reducing energy consumption for the system than traditional UAV mobile edge computing on the basis of maintaining queue stability.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3375345