一种基于无人机与智能反射面的隐蔽通信系统研究

刘学敏; 钱玉文; 宋耀良; 束锋; 陈魁宇; 朱捷伟

doi:10.11999/JEIT240663

一种基于无人机与智能反射面的隐蔽通信系统研究

doi: 10.11999/JEIT240663

1.
南京理工大学电子工程与光电技术学院南京 210094
2.
海南大学信息与通信工程学院海口 570228
3.
南京邮电大学电子与光学工程学院、柔性电子(未来技术)学院南京 210042
4.
中国联合网络通信有限公司苏州市分公司苏州 215100

基金项目: 国家重点研发计划“政府间国际科技创新合作”重点专项(2022YFE0122300)

详细信息

作者简介:
刘学敏：男，实验师，研究方向为电路与系统，通信信号处理等

钱玉文：男，副教授，研究方向为无线、量子隐蔽通信，智能通信等

宋耀良：男，教授，研究方向为人工电磁超材料，现代信号处理技术等

束锋：男，教授，研究方向为智能无线通信，智能无线信息安全传输等

陈魁宇：男，讲师，研究方向为电子对抗，智能无线通信等

朱捷伟：男，工程师，研究方向为智能无线通信，5G等

通讯作者:
钱玉文　admon@njust.edu.cn

中图分类号: TN918.82
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出版历程
- 收稿日期: 2024-07-29
- 修回日期: 2024-12-01
- 网络出版日期: 2024-12-09

An Intelligent Reflecting Surface Assisted Covert Communication System with a Cooperative Unmanned Aerial Vehicle

1.
School of Electronic and Opticcal Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2.
School of Information and Communication Engineering, Hainan University, Haikou 570228, China
3.
College of Electronic and Opticcal Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing 210094, China
4.
China United Network Communication Group Co., Ltd. Suzhou Branch, Suzhou 215100, China

Funds: The Key Special Project of “Intergovernmental International Scientific and Technological Innovation Cooperation” in the National Key Research and Development Program (2022YFE0122300)

摘要

摘要: 隐蔽通信可以在被监控的情况下安全传输数据，是网络安全领域重要分支。然而，实际通信系统具有通信环境复杂、覆盖范围广等特点，这使得隐蔽通信很难部署。为此，该文提出一种基于智能反射面(IRS)与无人机(UAV)辅助的无线隐蔽通信系统。引入智能反射表面作为中继节点转发发送者的信号，使用无人机作为发送者的友元节点，该友元节点通过发送人工噪声来干扰恶意用户对隐蔽通信的检测。在监听者接收噪声不确定的情况下，推导了最小错误检测概率，并与中断概率作为约束，以最大化隐蔽通信速率为目标，建立了系统的优化问题，采用Dinkelbach算法求解。仿真结果表明，当智能反射阵元的相位、干扰无人机的发射能量取得最优时，所提系统的隐蔽通信速率比单独配置智能反射表面的无线通信系统平均提高了37.9%，比单独配置无人机的系统评价速率提高了1.17倍。
- 信息安全 /
- 隐蔽通信 /
- 智能反射面 /
- 无人机
Abstract: Objective: Covert communication is a crucial area within network security, facilitating secure data transmission in monitored environments. Nevertheless, practical communication systems face challenges such as complex communication environments and extensive coverage areas. In recent years, Unmanned Aerial Vehicles (UAVs) have gained popularity in both commercial and military applications due to their flexibility, cost-effectiveness, and diverse applications. Additionally, Intelligent Reflection Surface (IRS)-assisted wireless communications have attracted significant attention, as IRS can be deployed in hostile communication environments while ensuring reliable transmission. Consequently, the exploration of hybrid IRS and UAV systems for the design of covert wireless communication systems presents a promising research avenue. Methods: This paper proposes a wireless covert communication system enhanced by an IRS and a UAV. In this configuration, the IRS functions as a relay node to transmit signals from the transmitter. The UAV serves as a cooperative relay node, facilitating not only the forwarding of covert messages to the intended receiver but also generating artificial noise to impede the detection of covert communication by malicious users. Under conditions of uncertainty regarding the received noise at the receiver, the minimum error detection probability is derived, and the system optimization problem is formulated with the objective of maximizing the covert communication rate while treating interruption probability as a constraint. Subsequently, the Dinkelbach-based approach is utilized to address the optimization problem. Results and Discussions: The key contributions of this research are as follows. First, a wireless covert communication system is developed using an IRS and an UAV. In this system, the IRS forwards covert messages from the transmitter to the receiver, while the UAV disrupts potential adversaries attempting to intercept secure communications. The integration of the IRS improves the covert communication rate, and the UAV-assisted design provides flexibility for deployment across diverse environments. The transmitter serves as the coordinator, managing both the UAV and IRS by transmitting control commands and collecting operational parameters. Second, the minimum detection error probability is derived under conditions of receiver uncertainty regarding noise, with the coordinates of the UAV and the transmitter assumed to be known. This derivation includes calculations of the False Alarm Probability (FAP) and the Missed Detection Probability (MDP) associated with the monitoring process. Third, a joint optimization problem is formulated to maximize the covert rate of the communication system. This problem optimizes the UAV's trajectory, the IRS phase, and the transmit power while satisfying constraints related to the derived minimum detection error probability, maximum transmit power, and UAV mobility. The problem is restructured into a convex formulation by dividing it into two steps: optimization of the transmit power and UAV trajectory. Fourth, an iterative algorithm is developed to address the optimization challenge, employing the Successive Convex Approximation (SCA) and Dinkelbach methods. The Dinkelbach method is used to reformulate the upper bound of the optimization variables into a convex problem. Simulation results demonstrate that the maximum covert rate is achieved when the IRS phase, UAV trajectory, and transmit power are jointly optimized. Conclusions: In conclusion, the research establishes the implementation of an IRS-aided covert communication system utilizing a cooperative UAV, suitable for deployment in complex environments. Additionally, a closed-form expression for the Directly Emitted Power (DEP) of covert communication for the monitoring device has been derived, taking into account the uncertainty of transmit power. A joint optimization problem has been formulated to optimize the phases of the IRS units, the jamming power of the UAV, and the transmitting power of the transmitter, while satisfying constraints related to the optimal DEP of Willie, the transmit power of the transmitter, and the transmit power of the AN. Simulation results indicate that the system's covertness and covert rate improve with an increased number of IRS units, extended UAV flight time, and higher interference power. Future research should also explore the deployment of this system in complex environments, focusing on the dynamic adjustment of the IRS phase units in conjunction with UAVs.
- Information security /
- Covert communication /
- Intelligent Reflecting Surface (IRS) /
- Unmanned Aerial Vehicle (UAV)

HTML全文

图 1 无人机辅助IRS隐蔽通信系统

下载: 全尺寸图片幻灯片

图 2 无人机轨迹优化图

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图 3 平均传输速率随各参数变化仿真图

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图 4 UAV不同飞行时间仿真图

下载: 全尺寸图片幻灯片

1 基于SCA和Dinkelbach技术的交替优化算法

(1) 初始化，R_b,0, $ \eta $和迭代索引参数k=1；
(2) 利用式(26)得到最优$ {\boldsymbol{\varTheta}} $。
(3) 重复
(4) 通过得到$ \left( {{{\boldsymbol{Q}}_{k - 1}},{\boldsymbol{\varTheta}} } \right) $，解决式(33)更新$ \left( {{P_k},{{\hat P}_{{\text{U}},k}}} \right) $；
(5) 根据求出的$ \left( {{P_k},{{\hat P}_{{\text{U}},k}}} \right) $，利用式(31)更新因子$ \eta $；
(6) 通过得到的$ \left( {{\boldsymbol{\varTheta}} ,{P_k},{{\hat P}_{{\text{U}},k}}} \right) $，利用式(24a)更新R_B,k；
(7) 设置k$ \leftarrow $k+1；
(8) 直到$ \left\| {{R_{B,k}} - {R_{B,k - 1}}} \right\| \le \kappa $。

下载: 导出CSV

表 1 仿真的具体参数设置

参数	参数描述	取值
N	无人机飞行时间	30 s
T	无人机飞行时隙个数	30
L	每个时隙持续时间	1 s
H	无人机的固定飞行高度	50 m
M	智能反射面反射单元个数	30
V_max	无人机的最大飞行速度	50 m/s
D	无人机每个时隙最大移动距离	50 m
β₀	信道距离为1米时的信道增益	–50 dB
$ \alpha $	路径损耗指数	2.2
D	天线间距	$ {\lambda \mathord{\left/ {\vphantom {\lambda 2}} \right. } 2} $
P_max	Alice的发射功率上限	1 W
$ {\hat P_{{\text{U}},\max }} $	无人机的最大AN功率上限	1 W
$ \sigma _{\text{W}}^2 $	Willie处噪声功率方差	–120 dBm
$ \sigma _{\text{B}}^2 $	Bob处噪声功率方差	–120 dBm
$ \varepsilon $	Willie确定所需隐蔽性的特定值	0.01
$ \kappa $	循环阈值	10^–5
w_B	Bob的地面坐标(m)	[–100,100]^T
w_W	Willie的地面坐标(m)	[100,100]^T
w_A	基站的地面坐标(m)	[–100,0]^T
q_A	无人机起点坐标(m)	[–300,20]^T
q_F	无人机终点坐标(m)	[300,20]^T

下载: 导出CSV

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