基于集成学习的全双工中继系统安全中继选择方案研究
Ensemble Learning-Based Relay Selection Scheme in Full-Duplex Relay System for Secure Transmission
- 电子学报 2021年49卷第9期 页码：1852-1856
- 作者机构：
重庆邮电大学通信与信息工程学院，重庆 400065
- 作者简介：
[ "张 梦 女,1988年10月出生于重庆市. 现为重庆邮电大学博士研究生. 主要研究方向为物理层安全技术. E-mail:mzhang@stu.cqupt.edu.cn" ]
[ "郑建宏 男,1961年8月出生于四川广安市. 现为重庆邮电大学教授. 主要研究方向为无线通信技术. E-mail:zhengjh@cqupt.edu.cn" ]
[ "刘香燕 女,1992年2月出生于四川资阳市. 现为重庆邮电大学博士研究生. 主要研究方向为资源分配管理.E-mail:xiangyan.leo@gmail.com" ]
[ "何 云 女,1979年11月出生于湖北武汉市. 现为重庆邮电大学博士研究生. 主要研究方向为新一代宽带无线通信系统. E-mail:yunhe@cqupt.edu.cn" ]
- 基金信息：国家科技重大专项(2018ZX03001026-002);重庆市教委科技项目(KJQN201800618;KJQN202002102)
DOI：10.12263/DZXB.20201340
中图分类号： TN918.91收稿：2020-11-26，
修回：2021-05-17，
纸质出版：2021-09-25
- 稿件说明：
移动端阅览
张梦,郑建宏,刘香燕等.基于集成学习的全双工中继系统安全中继选择方案研究[J].电子学报,2021,49(09):1852-1856.
ZHANG Meng,ZHENG Jian-hong,LIU Xiang-yan,et al.Ensemble Learning-Based Relay Selection Scheme in Full-Duplex Relay System for Secure Transmission[J].ACTA ELECTRONICA SINICA,2021,49(09):1852-1856.
张梦,郑建宏,刘香燕等.基于集成学习的全双工中继系统安全中继选择方案研究[J].电子学报,2021,49(09):1852-1856. DOI： 10.12263/DZXB.20201340.
ZHANG Meng,ZHENG Jian-hong,LIU Xiang-yan,et al.Ensemble Learning-Based Relay Selection Scheme in Full-Duplex Relay System for Secure Transmission[J].ACTA ELECTRONICA SINICA,2021,49(09):1852-1856. DOI： 10.12263/DZXB.20201340.
摘要
当处于无线信道状态信息（Channel State Information
CSI）快速变化或者多跳中继等应用场景时，利用集成学习算法解决安全中继选择问题能减少实时处理时延及计算复杂度.将合法信道和窃听信道的CSI作为训练模型输入，使系统安全容量到达最大的中继节点索引作为输出，把全双工中继系统安全中继选择问题转化为一个多类分类问题，并利用随机森林（Random Forest
RF）算法求解.安全中继选择方案的实现分为数据准备、模型建立和结果预测三个阶段.在数据准备阶段，由于RF算法要求训练模型输入为离散值，给出了均匀量化和非均匀量化两种特征提取法将CSI转化为离散值.最后，通过仿真实验验证方案性能.
Abstract
Using the ensemble learning algorithm to solve the secure relay selection problem can reduce the real-time processing delay and computational complexity
when the wireless systems that with fast-changing channel state information (CSI) or multi-hop relay situation. The secure relay selection problem of a full-duplex relay system is modeled as a multi-class classification problem
which is solved by the random forest (RF) algorithm. The CSI of the legitimate channel and eavesdropping channel is taken as the input of the training model
and the index of the relay node which can maximize the system security capacity is taken as the output of the training model. The implementation of the proposed scheme is divided into three phases: training data preparation
model building and result prediction. In the training data preparation phase
as the input features demanded by the RF algorithm are discrete values
feature extraction methods based on both uniform and non-uniform quantization algorithms are proposed to transform the CSI into finite discrete values. Finally
simulation experiments are conducted to verify the performance of the proposed scheme.
关键词
Keywords
references
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