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智能反射面辅助及人工噪声增强的无线隐蔽通信

周小波 于辉 彭旭 武庆庆 朱泽德 辜丽川

周小波, 于辉, 彭旭, 武庆庆, 朱泽德, 辜丽川. 智能反射面辅助及人工噪声增强的无线隐蔽通信[J]. 电子与信息学报, 2022, 44(7): 2392-2399. doi: 10.11999/JEIT211618
引用本文: 周小波, 于辉, 彭旭, 武庆庆, 朱泽德, 辜丽川. 智能反射面辅助及人工噪声增强的无线隐蔽通信[J]. 电子与信息学报, 2022, 44(7): 2392-2399. doi: 10.11999/JEIT211618
ZHOU Xiaobo, YU Hui, PENG Xu, WU Qingqing, ZHU Zede, GU Lichuan. Wireless Covert Communications Based on Intelligent Reflecting Surface Aided and Artificial Noise Enhanced[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2392-2399. doi: 10.11999/JEIT211618
Citation: ZHOU Xiaobo, YU Hui, PENG Xu, WU Qingqing, ZHU Zede, GU Lichuan. Wireless Covert Communications Based on Intelligent Reflecting Surface Aided and Artificial Noise Enhanced[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2392-2399. doi: 10.11999/JEIT211618

智能反射面辅助及人工噪声增强的无线隐蔽通信

doi: 10.11999/JEIT211618
基金项目: 国家自然科学基金(61901121, 61806187)
详细信息
    作者简介:

    周小波:男,1984年生,教授,博士生导师,研究方向为无人机通信、智能反射面通信、无线物理层安全和无线隐蔽通信等

    于辉:男,1994年生,硕士生,研究方向为智能反射面通信和无线隐蔽通信

    彭旭:男,1996年生,硕士生,研究方向为无人机通信和无线隐蔽通信

    武庆庆:男,1991年生,助理教授,博士生导师,研究方向为智能反射面通信、无人机通信、绿色物联网和机器学习

    朱泽德:男,1985年生,副研究员,硕士生导师,研究方向为人工智能与大数据

    辜丽川:男,1974年生,教授,博士生导师,研究方向为人工智能与大数据

    通讯作者:

    辜丽川 glc@ahau.edu.cn

  • 中图分类号: TN918.91

Wireless Covert Communications Based on Intelligent Reflecting Surface Aided and Artificial Noise Enhanced

Funds: The National Natural Science Foundation of China (61901121, 61806187)
  • 摘要: 该文考虑一种智能反射面(IRS)辅助及人工噪声(AN)增强的无线隐蔽通信以提升隐蔽传输性能。首先,分析了Willie的探测性能并给出了总的最小探测错误概率下界表达式。 在此基础之上,构建以最大化有效吞吐量为目标函数,以隐蔽需求和最大AN发射功率为约束的优化问题。该优化问题为非凸的,通常很难直接求解。该文提出基于Dinkelbach方法的交替迭代算法联合设计IRS的反射系数和Alice的发射功率及Bob的AN发射功率。为了降低计算复杂度,进一步提出一种低复杂度算法以获取相应优化变量的解析表达式。仿真结果表明:与无IRS及无AN方案相比,所提方案可以显著提升隐蔽传输性能。
  • 图  1  IRS辅助及AN增强的无线隐蔽通信系统模型

    图  2  IRS反射单元数量与${\gamma _{\text{b}}}$

    图  3  IRS的水平位置与${\gamma _{\rm{b}}}$的关系

    图  4  $P_{\rm{b}}^{\max }$$ {\gamma _{\text{b}}} $的关系

    表  1  基于Dinkelbach理论的迭代算法

     初始化参数$P_{\rm{b}}^{\max }$, $ K $, $ N $, $ \rho $, $ {\sigma _{\rm{b}}} $, $ {\sigma _{\rm{w}}} $, $ \beta $, $ {P_{\rm{a}}} $和$ {P_{\rm{b}}} $;设置收敛精
       度$ \varepsilon \ge 0 $; 设置$ {V_M} = 1 $和$ {V_D} = 1 $;
     (1) While $ |{V_M} - \beta | \ge \varepsilon $ do
     (2) 求解优化问题式(19)获得$ M $并计算目标函数值记为$ {V_M} $;
     (3) While $ |{V_D}| \ge \varepsilon $ do
          将步骤(3)所求最优的$ M $代入优化问题式(20),求解
          优化问题式(20)得到$ {P_{\rm{a}}} $和$ {P_{\rm{b}}} $;
          更新$ \beta = \dfrac{{{P_{\rm{a}}}{\rm{Tr}}({\boldsymbol{BM}})}}{{\rho {P_{\rm{b}}} + \sigma _{\text{b}}^2}} $;
          计算$ {V_D} = {P_{\rm{a}}}{\rm{Tr}}({\boldsymbol{BM}}) - \beta (\rho {P_{\rm{b}}} + \sigma _{\text{b}}^2) $;
        End While
     (4) 更新$ {P_{\rm{a}}} $和$ {P_{\rm{b}}} $
     (5) End While
    下载: 导出CSV

    表  2  低复杂度算法

     初始化:设置系统参数$ P_{\rm b}^{\max } $, $ K $, $ N $, $ \rho $,$ {\sigma _{\rm b}} $和${\sigma _{\rm{w}}}$;
     (1) 根据式(23)计算IRS反射系数$ u $;
     (2) if $\sigma _{\rm b}^2 \ge \dfrac{ {\rho \sigma _{\rm{w}}^2} }{ {|{ {\boldsymbol{u} }^{\rm{H} } }{\boldsymbol{c} } + {h_{ {\rm{bw} } } }{|^2} } }$ then
          $ P_{\rm b}^* = P_{\rm b}^{\max } $
       Else
          $ P_{\rm b}^* = 0 $;
       End if
     (3) 计算目标函数值;
     (4) End
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-24
  • 修回日期:  2022-04-20
  • 网络出版日期:  2022-04-25
  • 刊出日期:  2022-07-25

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