摘要
为保障多输入多输出窃听信道系统中信息传输的保密性,提出了一种基于机器学习的天线选择方案。首先利用机器学习解决分类问题准确率高、处理大数据高效这一优势,设计了基于奇异值分解的特征值提取、基于信干噪比的标签赋值方案,建立了k最近邻分类器和逻辑回归分类器选择最优天线最大化保密性能(可达保密速率和保密中断概率)。与传统天线选择方案相比,所提方案获得了几乎一致的保密性能,并且大幅降低了系统的选择复杂度和误比特率。
For guaranteeing the confidentiality of information transmission in multiple-input multiple-out-put( MIMO) wiretap channels,a machine learning based antenna selection( AS) scheme is proposed.Firstly,the AS in MIMO wiretap channels is characterized as multiclass-classification problem.Then the advantages of machine learning is utilized to solve classification problem with high accuracy and efficient processing of big data,and the scheme is designed including singular value decomposition based feature extraction and signal-to-interference plus noise ratio( SINR) based label assignment.The k-nearest neighbors classifier and logistic regression classifier are established to select the optimal antenna to maximize the secrecy performance( the achievable secrecy rate and the secrecy outage probability). Compared with conventional AS scheme,the machine learning based scheme can almost acquire the same secrecy performance with decreasing bit error rate( BER) of the system and selection complexity by a large margin.
作者
王博
钱蓉蓉
任文平
WANG Bo;QIAN Rongrong;REN Wenping(School of Information Science and Engineering,Yunnan University,Kunming 650500,China)
出处
《电讯技术》
北大核心
2020年第5期579-584,共6页
Telecommunication Engineering
基金
国家自然科学基金资助项目(61701433)
云南省科技厅面上项目(2018FB099)。
关键词
多输入多输出
物理层安全
机器学习
天线选择
k最近邻分类器
逻辑回归分类器
MIMO
physical layer security
machine learning
antenna selection
k-nearest neighbors classifier
logistic regression classifier
作者简介
王博,男,1992年生于陕西西安,硕士研究生,主要研究方向为天线选择技术;通信作者:钱蓉蓉,女,1985云年生于云南德宏,博士,讲师,主要研究方向为通信技术与信号处理技术,89313156@qq.com;任文平,女,1967年生于山东日照,副教授、硕士生导师,主要研究方向为FPGA在无线通信领域的应用。
