[1]郭歆莹,等.RIS辅助无人机通信系统波束赋形双重循环优化算法[J].郑州大学学报(工学版),2025,46(02):67-74.[doi:10.13705/j.issn.1671-6833.2025.02.005]
　GUO Xinying,,et al.Beamforming Dual-loop Optimization Algorithm for RIS-assisted UAV Communication Systems[J].Journal of Zhengzhou University (Engineering Science),2025,46(02):67-74.[doi:10.13705/j.issn.1671-6833.2025.02.005]
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RIS辅助无人机通信系统波束赋形双重循环优化算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
46
期数:
2025年02期
页码:
67-74
栏目:
出版日期:
2025-03-10

文章信息/Info

Title:
Beamforming Dual-loop Optimization Algorithm for RIS-assisted UAV Communication Systems
文章编号:
1671-6833(2025)02-0067-08
作者:
郭歆莹1;  2;  3;  刘龙飞1;  2;  3;  朱春华1;  2;  3
1.河南工业大学 粮食信息处理与控制教育部重点实验室,河南 郑州 450001;2.河南工业大学 河南省粮食光电探测与控制重点实验室,河南 郑州 450001;3.河南工业大学 信息科学与工程学院,河南 郑州 450001
Author(s):
GUO Xinying1;  2;  3;  LIU Longfei1;  2;  3;  ZHU Chunhua1;  2;  3
1.key Laboratory of Grain Information Processing and Control of Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; 2.Henan Key Laboratory of Grain Photoelectric Detection and Control, Henan University of Technology, Zhengzhou 450001, China; 3.College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
关键词:
无人机;  可重构智能表面;  波束赋形;  分式规划;  流形优化
Keywords:
UAV;  RIS;  beamforming;  fractional programming;  manifold optimization
分类号:
TN929.5
DOI:
10.13705/j.issn.1671-6833.2025.02.005
文献标志码:
A
摘要:
为提高复杂城市环境中无人机(UAV)集成可重构智能表面(RIS)构成的RIS-UAV中继通信系统的频谱效率,研究了定轨迹RIS-UAV中继通信系统的最大化系统下行和速率问题,构建了一个联合主被动波束赋形的多变量非凸优化问题。为了解决该问题,设计了一种基于分式规划(FP)的流形优化的低复杂度交替优化双重循环算法。首先,通过FP算法将问题化简;其次,利用标准凸优化算法设计基站主动波束赋形,利用流形优化算法设计RIS被动波束赋形;最后,通过内外双重循环交替迭代优化直至收敛。仿真结果表明:所提算法与经典方案对比具有较好的收敛性和较低的复杂度,且在最大发射功率为20 dBm时,和速率比随机相位方案实现大约6 dB的增益。此外,RIS采用3 bit离散相移时系统和速率性能与连续相移近似。
Abstract:
In order to enhance the spectrum efficiency of the RIS-UAV relay communication system formed by integrating UAV and RIS in complex urban environments, research was conducted to explore the optimization of maximizing system downlink sum rate of RIS-UAV relay communication system with fixed trajectory, and a multi-variable non-convex optimization problem of joint active and passive beamforming was constructed. To address this, a lowcomplexity AO dual-loop algorithm based on FP and manifold optimization was designed. Firstly, it was simplified by using the FP algorithm. Secondly, followed by the design of the base station′s active beamforming by using standard convex optimization algorithms, and the design of the RIS′s passive beamforming by using manifold optimization algorithms. Finally, the alternating iterative optimization through the dual internal and external loops continues until convergence is achieved. Simulation results demonstrated that the proposed algorithm had better convergence and lower complexity compared to classical schemes. Moreover, at a maximum transmission power of 20 dBm, the sum rate achieved approximately 6 dB gain over the random phase scheme. Additionally, when the RIS employed 3 bit discrete phase shift, the system′s sum rate performance was nearly equivalent to that with continuous phase shift.

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更新日期/Last Update: 2025-03-13