Deep Neural Network Assisted Approach for Antenna Selection in Untrusted Relay Networks

被引：18

作者：

Yao, Rugui ^{[1
]}

Zhang, Yuxin ^{[1
]}

Wang, Shengyao ^{[1
]}

Qi, Nan ^{[2
]}

Miridakis, Nikolaos I. ^{[3
,4
,5
]}

Tsiftsis, Theodoros A. ^{[3
,4
]}

机构：

[1] Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Peoples R China

[2] Nanjing Univ Aeronaut & Astronaut, Dept Elect Engn, Nanjing 210016, Peoples R China

[3] Jinan Univ, Sch Intelligent Syst Sci & Engn, Zhuhai 519070, Peoples R China

[4] Jinan Univ, Inst Phys Internet, Zhuhai 519070, Peoples R China

[5] Univ West Attica, Dept Elect & Elect Engn, Aegaleo 12244, Greece

来源：

IEEE WIRELESS COMMUNICATIONS LETTERS | 2019年 / 8卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Deep neural network (DNN); transmit antenna selection (TAS); untrusted relay networks;

D O I：

10.1109/LWC.2019.2933392

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This letter mainly studies the transmit antenna selection (TAS) scheme based on deep neural network (DNN) in untrusted relay networks. In our previous work, we revealed that machine learning (ML)-based TAS schemes have performance degradation caused by complicated coupling relationship between the achievable secrecy rate and channel gains. To solve this issue, we here introduce DNN to decouple the above complicated relationship. The simulation results show that the DNN scheme can achieve better decoupling and, thus, accomplish almost the same performance as the exhaustive searching scheme.

引用

页码：1644 / 1647

页数：4

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