Physical Layer Authentication of MIMO-STBC Systems Based on Constellation Dithering

被引:0
作者
An Y. [1 ]
Bai H. [1 ]
Zhang S. [1 ]
Ji Z. [1 ]
机构
[1] College of Artificial Intelligence, North China University of Science and Technology, Tangshan
来源
Intelligent and Converged Networks | 2023年 / 4卷 / 04期
关键词
channel state information (CSI); constellation dithering; hash function; MIMO-STBC; physical layer authentication;
D O I
10.23919/ICN.2023.0029
中图分类号
学科分类号
摘要
Most of the existing physical layer watermarking authentication schemes are based on a single-input single-output system and require pre-issue of shared keys. To address these problems, in this thesis, a physical layer authentication scheme without the distribution keys is proposed based on the constellation dithering physical layer authentication watermarking mechanism with a multiple-input multiple-output (MIMO) system, and space-time block coding (STBC) is used to improve the robustness of transmission. Specifically, the legitimate node obtains channel state information (CSI) through channel probing and couples CSI with the message signal using a hash function to generate an authentication tag, which is then embedded through constellation dithering. The receiver extracts the tag and authenticates it using hypothesis testing. Performance analysis shows that the scheme is resistant to various attacks such as replay, interference, tampering, and forgery. Simulation results show that the use of MIMO multi-antenna diversity with STBC coding technique reduces the bit error rate (BER) of message signals and tag signals and improves the detection rate of legitimate signals. © articles included in the journal are copyrighted to the ITU and TUP.
引用
收藏
页码:355 / 365
页数:10
相关论文
共 35 条
[1]  
Ran Y., Al-Shwaily H., Tang C., Tian G.Y., Johnston M., Physical layer authentication scheme with channel based tag padding sequence, Iet Commun., 13, 12, pp. 1776-1780, (2019)
[2]  
Xie N., Zhang S., Blind authentication at the physical layer under time-varying fading channels, Ieee J. Sel. Areas Commun., 36, 7, pp. 1465-1479, (2018)
[3]  
Chen Y., Ho P.-H., Wen H., Chang S.Y., Real S., On physical-layer authentication via online transfer learning, Ieee Internet Things J., 9, 2, pp. 1374-1385, (2022)
[4]  
An Y., Yue J., Chen L., Ji Z., Channel estimation for one-bit massive MIMO based on improved CGAN, J. Commun. Inf. Netw., 7, 2, pp. 214-220, (2022)
[5]  
Xie N., Chen C., Slope authentication at the physical layer, Ieee Trans. Inf. Forensics Secur., 13, 6, pp. 1579-1594, (2018)
[6]  
Xie X., Xu Z., Blind watermark detection based on KS test in radio-frequency signals, Electron. Lett., 56, 1, pp. 30-32, (2020)
[7]  
Alhoraibi L., Alghazzawi D., Alhebshi R., Rabie O.B.J., Physical layer authentication in wireless networksbased machine learning approaches, Sensors, 23, 4, (2023)
[8]  
Fang H., Wang X., Xu L., Fuzzy learning for multidimensional adaptive physical layer authentication: A compact and robust approach, Ieee Trans. Wirel. Commun., 19, 8, pp. 5420-5432, (2020)
[9]  
Lin B., Wang X., Yuan W., Wu N., A novel OFDM autoencoder featuring CNN-based channel estimation for Internet of vessels, Ieee Internet Things J., 7, 8, pp. 7601-7611, (2020)
[10]  
Wang S., Huang K., Xu X., Zhong Z., Zhou Y., CSIbased physical layer authentication via deep learning, Ieee Wirel. Commun. Lett., 11, 8, pp. 1748-1752, (2022)