Resource Allocation of IRS-Assisted UAV Covert Communication Systems with User Fairness Consideration

WANG Zheng-qiang; LI Chun; REN Xin-zhi; XU Yong-jun

doi:10.12263/DZXB.20240937

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Resource Allocation of IRS-Assisted UAV Covert Communication Systems with User Fairness Consideration

PAPERS | 更新时间：2026-02-10

- Resource Allocation of IRS-Assisted UAV Covert Communication Systems with User Fairness Consideration
- ACTA ELECTRONICA SINICA Vol. 53, Issue 11, Pages: 4104-4115(2025)
- 作者机构：
  
  重庆邮电大学信息与通信工程学院，重庆 400065
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(U23A20279;62271094)
- DOI：10.12263/DZXB.20240937
  CLC： TN929.5;
- Received：17 October 2024，
  
  Accepted：11 November 2025，
  
  Published：25 November 2025
- 稿件说明：
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王正强, 李淳, 任昕志, 等. 基于用户公平性的IRS辅助无人机隐蔽通信系统资源分配算法[J]. 电子学报, 2025, 53(11): 4104-4115.

WANG Zheng-qiang, LI Chun, REN Xin-zhi, et al. Resource Allocation of IRS-Assisted UAV Covert Communication Systems with User Fairness Consideration[J]. Acta Electronica Sinica, 2025, 53(11): 4104-4115.
王正强, 李淳, 任昕志, 等. 基于用户公平性的IRS辅助无人机隐蔽通信系统资源分配算法[J]. 电子学报, 2025, 53(11): 4104-4115. DOI：10.12263/DZXB.20240937

WANG Zheng-qiang, LI Chun, REN Xin-zhi, et al. Resource Allocation of IRS-Assisted UAV Covert Communication Systems with User Fairness Consideration[J]. Acta Electronica Sinica, 2025, 53(11): 4104-4115. DOI：10.12263/DZXB.20240937

摘要

无人机（Unmanned Aerial Vehicle，UAV）通信与智能反射表面（Intelligent Reflecting Surface，IRS）作为第六代移动通信系统中的两项关键技术，凭借其高机动性与智能波束重构能力，为构建高可靠、高安全的下一代无线网络提供了全新范式.然而，无线信道的广播特性使无人机的安全通信面临严峻挑战.特别是在多用户场景下，如何在进行安全传输的同时，保障多个合法用户的服务公平性，成为一个复杂且亟待解决的关键问题.该文研究了一个IRS辅助的多天线无人机隐蔽通信系统，旨在解决多用户间资源分配不均衡的难题.在考虑用户公平性的前提下，将最大化所有合法用户中最差用户的最小平均隐蔽速率作为优化目标，确保所有用户都能获得可接受的最低服务质量.该问题通过联合优化用户调度、无人机飞行轨迹、多天线发射波束赋形以及IRS的相移矩阵进行求解.由于该优化问题是一个高度非凸且变量紧密耦合，难以采用凸优化方法直接求解.为此，本文设计了一种基于块坐标下降的迭代算法，将原问题解耦为四个相对易于处理的子问题，并针对各子问题的特性，分别采用了连续凸近似、二次变换、放缩以及变量替换等方法，将其转化为凸优化问题，进而通过交替优化进行高效求解.仿真结果表明，所提算法具有快速收敛性.与无IRS辅助、无轨迹优化等基准方案相比，所提出的联合优化算法能显著提升系统用户的最小平均隐蔽速率.

Abstract

Unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS) are two key technologies in the sixth-generation mobile communication system. With their high mobility and intelligent beamforming capabilities

they offer a new paradigm for building highly reliable and secure next-generation wireless networks. However

the broadcast nature of wireless channels poses severe challenges to the secure communication of UAV. Particularly in multi-user scenarios

how to ensure secure transmission while maintaining service fairness among multiple legitimate users is a complex and urgent problem to be solved. This paper studies an IRS-assisted multi-antenna UAV covert communication system

aiming to address the issue of unbalanced resource allocation among multiple users. Under the premise of considering user fairness

this paper takes maximizing the minimum average covert rate of the worst legitimate user as the optimization objective

ensuring that all users can obtain an acceptable minimum quality of service. This problem is solved by jointly optimizing user scheduling

the three-dimensional flight trajectory of the UAV

multi-antenna transmit beamforming

and the phase shift matrix of the IRS. Due to the highly non-convex and tightly coupled nature of the optimization problem

it is difficult to solve directly by convex optimization method. Therefore

this paper designs an efficient iterative algorithm based on block coordinate descent

decoupling the original problem into four relatively easy-to-handle sub-problems. According to the characteristics of each sub-problem

methods such as successive convex approximation

quadratic transformation

scaling

and variable substitution are adopted to transform them into convex optimization problems

and then solved efficiently through an alternating optimization mechanism. Simulation results show that the proposed algorithm has fast convergence rate. Compared with the benchmark schemes without IRS assistance and without trajectory optimization

the proposed joint optimization algorithm can significantly improve the minimum average covert rate of system users.

关键词

Keywords

references

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