智能反射面輔助及人工噪聲增強的無線隱蔽通信

周小波; 于輝; 彭旭; 武慶慶; 朱澤德; 辜麗川

doi:10.11999/JEIT211618

智能反射面輔助及人工噪聲增強的無線隱蔽通信

doi: 10.11999/JEIT211618

周小波^{1, 2},
于輝²,
彭旭²,
武慶慶³,
朱澤德⁴,
辜麗川^1, ,

1.
安徽農(nóng)業(yè)大學(xué)信息與計算機學(xué)院合肥 230036
2.
阜陽師范大學(xué)物理與電子工程學(xué)院阜陽 236037
3.
澳門大學(xué)智慧城市物聯(lián)網(wǎng)國家重點實驗室澳門 999078
4.
中國科學(xué)院合肥物質(zhì)科學(xué)研究院合肥 230031

基金項目: 國家自然科學(xué)基金(61901121, 61806187)

詳細信息

作者簡介:
周小波：男，1984年生，教授，博士生導(dǎo)師，研究方向為無人機通信、智能反射面通信、無線物理層安全和無線隱蔽通信等

于輝：男，1994年生，碩士生，研究方向為智能反射面通信和無線隱蔽通信

彭旭：男，1996年生，碩士生，研究方向為無人機通信和無線隱蔽通信

武慶慶：男，1991年生，助理教授，博士生導(dǎo)師，研究方向為智能反射面通信、無人機通信、綠色物聯(lián)網(wǎng)和機器學(xué)習(xí)

朱澤德：男，1985年生，副研究員，碩士生導(dǎo)師，研究方向為人工智能與大數(shù)據(jù)

辜麗川：男，1974年生，教授，博士生導(dǎo)師，研究方向為人工智能與大數(shù)據(jù)

通訊作者:
辜麗川　glc@ahau.edu.cn

中圖分類號: TN918.91
計量
- 文章訪問數(shù): 1928
- HTML全文瀏覽量: 973
- PDF下載量: 278
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2021-12-24
- 修回日期: 2022-04-20
- 網(wǎng)絡(luò)出版日期: 2022-04-25
- 刊出日期: 2022-07-25

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

ZHOU Xiaobo^{1, 2},
YU Hui²,
PENG Xu²,
WU Qingqing³,
ZHU Zede⁴,
GU Lichuan^{1
, ,}

1.
School of Information and Computer, Anhui Agricultural University, Hefei 230036, China
2.
School of Physics and Electronic Engineering, Fuyang Normal University, Fuyang 236037, China
3.
State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao 999078, China
4.
Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China

Funds: The National Natural Science Foundation of China (61901121, 61806187)

摘要

摘要: 該文考慮一種智能反射面(IRS)輔助及人工噪聲(AN)增強的無線隱蔽通信以提升隱蔽傳輸性能。首先，分析了Willie的探測性能并給出了總的最小探測錯誤概率下界表達式。在此基礎(chǔ)之上，構(gòu)建以最大化有效吞吐量為目標(biāo)函數(shù)，以隱蔽需求和最大AN發(fā)射功率為約束的優(yōu)化問題。該優(yōu)化問題為非凸的，通常很難直接求解。該文提出基于Dinkelbach方法的交替迭代算法聯(lián)合設(shè)計IRS的反射系數(shù)和Alice的發(fā)射功率及Bob的AN發(fā)射功率。為了降低計算復(fù)雜度，進一步提出一種低復(fù)雜度算法以獲取相應(yīng)優(yōu)化變量的解析表達式。仿真結(jié)果表明：與無IRS及無AN方案相比，所提方案可以顯著提升隱蔽傳輸性能。
- 隱蔽通信 /
- 智能反射面 /
- 全雙工 /
- 人工噪聲
Abstract: In this work, an Intelligent Reflecting Surface (IRS) aided and Artificial Noise (AN) enhanced covert wireless communications is considered to improve the covert transmission performance. Firstly, the detection performance at Willie is analyzed and a lower bound on Willie’s minimum total detection error probability is presented. On this basis, an optimization problem that maximizes the effective throughput subject to the covertness constraint and the maximum AN transmit power constraint is formulated. The optimization problem is non-convex, which is generally difficult to tackle directly. Then, an alternating iterative algorithm based on Dinkelbach method is proposed to jointly design the IRS reflection beamforming and Alice’s transmit power together with Bob’s AN transmit power. In order to reduce the computational complexity, a low-complexity algorithm is further proposed to obtain analytical expressions for the corresponding optimization variables. Simulation results show that the proposed scheme improves significantly the covert transmission performance compared with the schemes without IRS and without AN.
- Covert communications /
- Intelligent Reflecting Surface(IRS) /
- Full-duplex /
- Artificial Noise(AN)

HTML全文

圖 1 IRS輔助及AN增強的無線隱蔽通信系統(tǒng)模型

下載: 全尺寸圖片幻燈片

圖 2 IRS反射單元數(shù)量與${\gamma _{\text}}$

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圖 3 IRS的水平位置與${\gamma _{\rm}}$的關(guān)系

下載: 全尺寸圖片幻燈片

圖 4 $P_{\rm}^{\max }$與$ {\gamma _{\text}} $的關(guān)系

下載: 全尺寸圖片幻燈片

表 1 基于Dinkelbach理論的迭代算法

　初始化參數(shù)$P_{\rm}^{\max }$, $ K $, $ N $, $ \rho $, $ {\sigma _{\rm}} $, $ {\sigma _{\rm{w}}} $, $ \beta $, $ {P_{\rm{a}}} $和$ {P_{\rm}} $；設(shè)置收斂精
　　度$ \varepsilon \ge 0 $；設(shè)置$ {V_M} = 1 $和$ {V_D} = 1 $；

　(1) While $ |{V_M} - \beta | \ge \varepsilon $ do

　(2) 求解優(yōu)化問題式(19)獲得$ M $并計算目標(biāo)函數(shù)值記為$ {V_M} $；

　(3) While $ |{V_D}| \ge \varepsilon $ do

　　　　　　將步驟(3)所求最優(yōu)的$ M $代入優(yōu)化問題式(20)，求解
　　　　　　優(yōu)化問題式(20)得到$ {P_{\rm{a}}} $和$ {P_{\rm}} $；
　　　　　　更新$ \beta = \dfrac{{{P_{\rm{a}}}{\rm{Tr}}({\boldsymbol{BM}})}}{{\rho {P_{\rm}} + \sigma _{\text}^2}} $；

　　　　　　計算$ {V_D} = {P_{\rm{a}}}{\rm{Tr}}({\boldsymbol{BM}}) - \beta (\rho {P_{\rm}} + \sigma _{\text}^2) $；

　　　 End While

　(4) 更新$ {P_{\rm{a}}} $和$ {P_{\rm}} $

　(5) End While

下載: 導(dǎo)出CSV

表 2 低復(fù)雜度算法

　初始化：設(shè)置系統(tǒng)參數(shù)$ P_{\rm b}^{\max } $, $ K $, $ N $, $ \rho $,$ {\sigma _{\rm b}} $和${\sigma _{\rm{w}}}$；

　(1) 根據(jù)式(23)計算IRS反射系數(shù)$ 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) 計算目標(biāo)函數(shù)值；

　(4) End

下載: 導(dǎo)出CSV

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