Energy efficient resource allocation of IRS-Assisted UAV network

Shuang Zhang; Songwen Gu; Yucong Zhou; Lei Shi; Huilong Jin; Shuang Zhang; Songwen Gu; Yucong Zhou; Lei Shi; Huilong Jin

doi:10.3934/era.2024217

Electronic Research Archive

2024, Volume 32, Issue 7: 4753-4771. doi: 10.3934/era.2024217

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Research article Special Issues

Energy efficient resource allocation of IRS-Assisted UAV network

1.
College of Engineering, Hebei Normal University, Shijiazhuang 050024, China
2.
Hebei Provincial Key Laboratory of Information Fusion and Intelligent Control, Shijiazhuang 050024, China
3.
Key Laboratory of Education Informatization for Nationalities, Yunnan Normal University, Ministry of Education, Kunming 650092, China
4.
Hebei Provincial Innovation Center for Wireless Sensor Network Data Application Technology, Shijiazhuang 050024, China

Received: 16 May 2024 Revised: 13 July 2024 Accepted: 24 July 2024 Published: 30 July 2024

The integration of unmanned aerial vehicle (UAV) networks with intelligent reflecting surface (IRS) technology offers a promising solution to enhance wireless communication by dynamically altering signal propagation. This study addresses the challenge of maximizing system energy efficiency (EE) in IRS-assisted UAV networks. The primary objective is to optimize power allocation and IRS reflection design to achieve this goal. To tackle the optimization problem, we employ a block coordinate descent (BCD) method, decomposing it into three subproblems: phase shift optimization, power allocation, and trajectory planning. These subproblems are iteratively solved using an improved particle swarm optimization (PSO) algorithm. Simulation results demonstrate that the proposed PSO algorithm effectively plans high-quality UAV trajectories in complex environments, significantly enhancing EE. The findings suggest that the IRS-assisted UAV model outperforms traditional UAV models, offering substantial improvements in wireless communication quality and EE.
- UAV,
- IRS,
- EE,
- power allocation,
- trajectory optimization,
- PSO
Citation: Shuang Zhang, Songwen Gu, Yucong Zhou, Lei Shi, Huilong Jin. Energy efficient resource allocation of IRS-Assisted UAV network[J]. Electronic Research Archive, 2024, 32(7): 4753-4771. doi: 10.3934/era.2024217

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Abstract

The integration of unmanned aerial vehicle (UAV) networks with intelligent reflecting surface (IRS) technology offers a promising solution to enhance wireless communication by dynamically altering signal propagation. This study addresses the challenge of maximizing system energy efficiency (EE) in IRS-assisted UAV networks. The primary objective is to optimize power allocation and IRS reflection design to achieve this goal. To tackle the optimization problem, we employ a block coordinate descent (BCD) method, decomposing it into three subproblems: phase shift optimization, power allocation, and trajectory planning. These subproblems are iteratively solved using an improved particle swarm optimization (PSO) algorithm. Simulation results demonstrate that the proposed PSO algorithm effectively plans high-quality UAV trajectories in complex environments, significantly enhancing EE. The findings suggest that the IRS-assisted UAV model outperforms traditional UAV models, offering substantial improvements in wireless communication quality and EE.

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