基于用戶竊聽的MU-MISO反向散射通信系統(tǒng)魯棒資源分配算法

徐勇軍; 徐然; 周繼華; 陳量; 黃東

doi:10.11999/JEIT221508

基于用戶竊聽的MU-MISO反向散射通信系統(tǒng)魯棒資源分配算法

doi: 10.11999/JEIT221508 cstr: 32379.14.JEIT221508

徐勇軍^{1, 2, ,},
徐然¹,
周繼華²,
陳量²,
黃東³

1.
重慶郵電大學通信與信息工程學院重慶 400065
2.
重慶金美通信有限責任公司重慶 400030
3.
貴州大學現(xiàn)代制造技術(shù)教育部重點實驗室貴陽 550025

基金項目: 國家自然科學基金(62271094)，重慶市教委科學技術(shù)研究項目(KJZD-K202200601)，重慶市自然科學基金創(chuàng)新發(fā)展聯(lián)合基金重點項目(CSTB2022NSCQ-LZX0009)

詳細信息

作者簡介:
徐勇軍：男，副教授，博士生導(dǎo)師，研究方向為反向散射通信、魯棒資源分配

徐然：男，碩士生，研究方向為反向散射、魯棒資源分配

周繼華：男，研究員，博士生導(dǎo)師，研究方向為無線網(wǎng)絡(luò)、資源分配等

陳量：男，正高級工程師，研究方向為軟件定義網(wǎng)絡(luò)技術(shù)等

通訊作者:
徐勇軍　xuyj@cqupt.edu.cn

中圖分類號: TN929
計量
- 文章訪問數(shù): 303
- HTML全文瀏覽量: 207
- PDF下載量: 70
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2022-12-05
- 修回日期: 2023-09-24
- 網(wǎng)絡(luò)出版日期: 2023-10-18
- 刊出日期: 2024-01-17

Robust Resource Allocation Algorithm in MU-MISO Backscatter Communication Systems with Eavesdroppers

XU Yongjun^{1, 2
, ,},
XU Ran¹,
ZHOU Jihua²,
CHEN Liang²,
HUANG Dong³

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Jinmei Communication Co. LTD., Chongqing 400030, China
3.
Key Laboratory of Advanced Manufacturing Technology of Ministry of Education, GuiZhou University, Guiyang 550025, China

Funds: The National Natural Science Foundation of China (62271094), The Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJZD-K202200601), The Key Fund of Natural Science Foundation of Chongqing (CSTB2022NSCQ-LZX0009)

摘要

摘要: 針對反向散射通信系統(tǒng)信道估計不準、信息容易被竊聽等問題，該文提出一種基于用戶竊聽的多用戶-多輸入單輸出(MU-MISO)反向散射通信系統(tǒng)魯棒資源分配算法，以提高系統(tǒng)傳輸魯棒性與信息安全性。首先，考慮基站最大功率、時間分配、信道不確定性、能量收集和保密率等約束，建立一個MU-MISO的反向散射通信系統(tǒng)魯棒資源分配問題。其次，基于非線性能量收集模型和有界球形信道不確定性模型，利用變量松弛法和S過程將原NP-hard問題轉(zhuǎn)化為確定性問題，隨后利用連續(xù)凸近似、半正定松弛與塊坐標下降法將其轉(zhuǎn)化為凸優(yōu)化問題求解。仿真結(jié)果表明，與傳統(tǒng)非魯棒算法對比，所提算法具有較高的系統(tǒng)容量和較低的中斷概率。
- 反向散射通信 /
- 物理層安全 /
- 非線性能量收集 /
- 魯棒資源分配
Abstract: Focusing on the problems of inaccurate channel estimation and easy eavesdropping of information in backscatter communication systems, a robust resource allocation algorithm for Multi-User Multi-Input Single-Output (MU-MISO) backscatter communication systems based on user eavesdropping is proposed to improve the transmission robustness and information security of the systems. Firstly, considering the constraints on the maximum power of the base station, time allocation, channel uncertainties, energy collection, and security rate, a robust resource allocation problem for MU-MISO backscatter communication systems is established. Secondly, based on the nonlinear energy harvesting model and the bounded spherical uncertainty model, the original NP-hard problem is transformed into a deterministic one by using the variable relaxation and S-Procedure methods, and then it is transformed into a convex optimization problem by using successive convex approximation, semi-positive definite relaxation and block coordinate descent methods. Simulation results show that the proposed algorithm has higher system capacity and lower outage probability compared with the traditional non-robust algorithm.
- Backscatter communication /
- Physical layer security /
- Nonlinear energy harvesting model /
- Robust resource allocation

HTML全文

圖 1 反向散射通信物理層安全網(wǎng)絡(luò)

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圖 2 本文算法收斂性能

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圖 3 最大發(fā)射功率和反射系數(shù)對吞吐量的影響

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圖 4 信道不確定性與噪聲功率對保密率的影響

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圖 5 反射系數(shù)與信道不確定性對保密吞吐量的影響

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圖 6 信道不確定性與反射系數(shù)對保密吞吐量的影響

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圖 7 信道不確定性對中斷概率的影響

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算法1　基于塊坐標下降法的魯棒資源分配迭代算法
1. 初始化系統(tǒng)參數(shù)：$ K,\,M,\,{\beta _k},\,E_k^{\text{C}},\,\sigma _k^2,\,\sigma _e^2,\,T,\,\bar x_k^2,\,\bar x_k^3,\,\bar y_k^1,\,\bar \gamma _k^{\text{E}},\,\alpha _k^{\left( 0 \right)},\,{R^{{\text{sum}}(0)}} $；閾值：$r_k^{\min },\,{P^{\max }}$；估計誤差上界：$\varepsilon $；收斂精度：$ \varpi $；　初始化迭代次數(shù)：$l{\text{ = }}1$。
2. Repeat
3. 　固定$ \alpha _k^{\left( 0 \right)} $，求解子問題1，獲得$ {\boldsymbol{W}}_k^{\left( l \right) * },\,{{\boldsymbol{Z}}^{\left( l \right) * }} $。
if $ {\boldsymbol{W}}_k^{\left( l \right) * } $滿足$ \text{Rank}\left({{\boldsymbol{W}}}_{k}^{\left(l\right)\ast }\right)=1 $，可以通過特征值分解獲得最優(yōu)波束向量，即${\boldsymbol{W}}_k^{\left( l \right) * }{\text{ = }}{\boldsymbol{w}}_k^{\left( l \right) * }{\boldsymbol{w}}_k^{\left( l \right) * {\text{H}}}$。
else if ${\boldsymbol{W}}_k^{\left( l \right) * }$的秩大于1，可以通過高斯隨機化獲得最優(yōu)向量。
4. 　固定${\boldsymbol{W}}_k^{\left( l \right) * },\,{{\boldsymbol{Z}}^{\left( l \right) * }}$，求解子問題2，獲得$ \alpha _k^{\left( l \right) * } $。
5. 　計算$ {R^{{\text{sum}}(l)}} $，并且更新迭代次數(shù)$l = l + 1$。
6. Until $ \left\| {{R^{{\text{sum}}(l)}} - {R^{{\text{sum}}(l - 1)}}} \right\| \le \varpi $。
7. Return $ {\boldsymbol{w}}_k^{{\text{opt}}}{\text{ = }}{\boldsymbol{w}}_k^{\left( l \right) * },\,{{\boldsymbol{Z}}^{{\text{opt}}}} = {{\boldsymbol{Z}}^{\left( l \right) * }},\,\alpha _k^{{\text{opt}}} = \alpha _k^{\left( l \right) * },\,{R^{{\text{sum}}}} = {R^{{\text{sum}}(l)}} $。

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