A Comprehensive Survey on Physical Layer Authentication Techniques: Categorization and Analysis of Model-Driven and Data-Driven Approaches
Authors: 
Zhifan Lai, Zikai Chang, Mingrui Sha, Qihong Zhang, Ning Xie, Changsheng Chen, Dusit (Tao) NiyatoAuthors Info & Claims
Zhifan Lai, Zikai Chang, Mingrui Sha, Qihong Zhang, Ning Xie, Changsheng Chen, Dusit (Tao) NiyatoAuthors Info & ClaimsArticle No.: 117, Pages 1 - 35
Abstract
The open and broadcast nature of wireless mediums introduces significant security vulnerabilities, making authentication a critical concern in wireless networks. In recent years, Physical-Layer Authentication (PLA) techniques have garnered considerable research interest due to their advantages over Upper-Layer Authentication (ULA) methods, such as lower complexity, enhanced security, and greater compatibility. The application of signal processing techniques in PLA serves as a crucial link between the extraction of Physical-Layer Features (PLFs) and the authentication of received signals. Different signal processing approaches, even with the same PLF, can result in varying authentication performances and computational demands. Despite this, there remains a shortage of comprehensive overviews on state-of-the-art PLA schemes with a focus on signal processing approaches. This article presents the first thorough survey of signal processing in various PLA schemes, categorizing existing approaches into model-based and Machine Learning (ML)-based schemes. We discuss motivation and address key issues in signal processing for PLA schemes. The applications, challenges, and future research directions of PLA are discussed in Part 3 of the Appendix, which can be found in supplementary materials online.
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References
[1]
Liza Afeef, Haji M. Furqan, and Hüseyin Arslan. 2022. Physical layer authentication scheme in beamspace MIMO systems. IEEE Commun. Lett. 26 (2022), 1484–1488.
[2]
Waqas Aman, Saif Al-Kuwari, and Marwa Qaraqe. 2023. Location-based physical layer authentication in underwater acoustic communication networks. In IEEE Vehicular Technology Conference (VTC), Florence, Italy, 1–6.
[3]
W. Aman, M. M. U. Rahman, J. Qadir, H. Pervaiz, and Q. Ni. 2018. Impersonation detection in line-of-sight underwater acoustic sensor networks. IEEE Access 6 (2018), 44459–44472.
[4]
L. Bai, L. Zhu, J. Liu, J. Choi, and W. Zhang. 2020. Physical layer authentication in wireless communication networks: A survey. J. Commun. Inf. Netw. 5, 3 (2020), 237–264.
[5]
S. Balakrishnan, S. Gupta, A. Bhuyan, P. Wang, D. Koutsonikolas, and Z. Sun. 2020. Physical layer identification based on spatial-temporal beam features for millimeter-wave wireless networks. IEEE Trans. Inf. Forens. Secur. 15 (2020), 1831–1845.
[6]
Gianmarco Baldini, Raimondo Giuliani, and Gary Steri. 2018. Physical layer authentication and identification of wireless devices using the synchrosqueezing transform. Appl. Sci. 8, 11 (2018).
[7]
G. Baldini, R. Giuliani, G. Steri, and R. Neisse. 2017. Physical layer authentication of internet of things wireless devices through permutation and dispersion entropy 2. In Global Internet of Things Summit (GIoTS’17). 1–6.
[8]
G. Baldini and G. Steri. 2017. A survey of techniques for the identification of mobile phones using the physical fingerprints of the built-in components. IEEE Commun. Surv. Tutor. 19, 3 (2017), 1761–1789.
[9]
Christoph Bandt and Bernd Pompe. 2002. Permutation entropy: A natural complexity measure for time series. Phys. Rev. Lett. 88, 17 (2002), 174102.
[10]
Jianrong Bao, Xiqi Gao, Xiaorong Xu, Rui Guo, and Bin Jiang. 2016. Low rate QC LDPC codes with reconfigurable structures for space information networks. J. Commun. 11, 3 (2016), 255–262.
[11]
Bert den Boer. 1993. A simple and key-economical unconditional authentication scheme. J. Comput. Secur. 2 (1993), 65–72.
[12]
D. Chen, N. Zhang, R. Lu, X. Fang, K. Zhang, Z. Qin, and X. Shen. 2018. An LDPC code based physical layer message authentication scheme with prefect security. IEEE J. Select. Areas Commun. 36, 4 (2018), 748–761.
[13]
M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah. 2019. Artificial neural networks-based machine learning for wireless networks: A tutorial. IEEE Commun. Surv. Tutor. 21, 4 (2019), 3039–3071.
[14]
S. Chen, Z. Pang, H. Wen, K. Yu, T. Zhang, and Y. Lu. 2021. Automated labeling and learning for physical layer authentication against clone node and Sybil attacks in industrial wireless edge networks. IEEE Trans. Industr. Inform. 17, 3 (2021), 2041–2051.
[15]
Songlin Chen, Hong Wen, Jinsong Wu, Jie Chen, Wenjie Liu, Lin Hu, and Yi Chen. 2018. Physical-layer channel authentication for 5G via machine learning algorithm. Wirel. Commun. Mob. Comput. (2018), 1–10.
[16]
Y. Chen, P. H. Ho, H. Wen, S. Y. Chang, and S. Real. 2022. On physical-layer authentication via online transfer learning. IEEE Internet Things J. 9, 2 (2022), 1374–1385.
[17]
Y. Chen, S. Real, H. Wen, B. Cheng, W. Wang, P. H. Ho, and S. Y. Chang. 2020. On physical-layer authentication via triple pool convolutional neural network. In IEEE Globecom Workshops (GC Wkshps’20). 1–6.
[18]
Longwang Cheng, Li Zhou, Boon-Chong Seet, Wei Li, Dongtang Ma, and Jibo Wei. 2017. Efficient physical-layer secret key generation and authentication schemes based on wireless channel-phase. Mob. Inf. Syst. 2017 (2017), 7393526.
[19]
Zicheng Chi, Yan Li, Hongyu Sun, Yao Yao, Zheng Lu, and Ting Zhu. 2016. B2W2: N-way concurrent communication for IoT devices. In 14th ACM Conference on Embedded Network Sensor Systems CD-ROM (SenSys’16). Association for Computing Machinery, New York, USA, 245–258. DOI:
[20]
Keunwoo Choi, György Fazekas, Mark Sandler, and Kyunghyun Cho. 2017. Convolutional recurrent neural networks for music classification. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’17). IEEE, 2392–2396.
[21]
Boris Danev, Davide Zanetti, and Srdjan Capkun. 2012. On physical-layer identification of wireless devices. ACM Comput. Surv. 45, 1 (2012), Article 6.
[22]
Roy De Maesschalck, Delphine Jouan-Rimbaud, and Désiré L. Massart. 2000. The Mahalanobis distance. Chemomet. Intell. Lab. Syst. 50, 1 (2000), 1–18.
[23]
Whitfield Diffie and Martin E. Hellman. 2022. New directions in cryptography. In Democratizing Cryptography: The Work of Whitfield Diffie and Martin Hellman. Association for Computing Machinery, New York, NY, USA, 365–390.
[24]
Ruizhong Du, Lin Zhen, and Yan Liu. 2023. Physical layer authentication based on integrated semi-supervised learning in wireless networks for dynamic industrial scenarios. IEEE Trans. Vehic. Technol. 72, 5 (2023), 6154–6164. DOI:
[25]
H. Fang, X. Wang, and L. Hanzo. 2019. Learning-aided physical layer authentication as an intelligent process. IEEE Trans. Commun. 67, 3 (2019), 2260–2273.
[26]
H. Fang, X. Wang, and S. Tomasin. 2019. Machine learning for intelligent authentication in 5G and beyond wireless networks. IEEE Wirel. Commun. 26, 5 (2019), 55–61.
[27]
Gerard J. Foschini and Michael J. Gans. 1998. On limits of wireless communications in a fading environment when using multiple antennas. Wirel. Person. Commun. 6 (1998), 311–335.
[28]
Yoav Freund and Robert E. Schapire. 1997. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 1 (1997), 119–139. DOI:
[29]
Demin Gao, Liyuan Ou, Yongrui Chen, Xiuzhen Guo, Ruofeng Liu, Yunhuai Liu, and Tian He. 2024. Physical-layer CTC from BLE to Wi-Fi with IEEE 802.11ax. IEEE Trans. Mob. Comput. (2024), 1–14. DOI:
[30]
Demin Gao, Shuai Wang, Yunhuai Liu, Wenchao Jiang, Zhijun Li, and Tian He. 2021. Spoofing-jamming attack based on cross-technology communication for wireless networks. Comput. Commun. 177 (2021), 86–95.
[31]
K. St. Germain and F. Kragh. 2020. Multi-transmitter physical layer authentication using channel state information and deep learning. In International Conference on Signal Processing and Communication Systems (ICSPCS’20). 1–8.
[32]
K. St. Germain and F. Kragh. 2020. Physical-layer authentication using channel state information and machine learning. In International Conference on Signal Processing and Communication Systems (ICSPCS’20). 1–8.
[33]
K. S. Germain and F. Kragh. 2021. Channel prediction and transmitter authentication with adversarially-trained recurrent neural networks. IEEE Open J. Commun. Societ. 2 (2021), 964–974.
[34]
K. S. Germain and F. Kragh. 2021. Mobile physical-layer authentication using channel state information and conditional recurrent neural networks. In IEEE Vehicular Technology Conference (VTC’21). 1–6.
[35]
Kia Ghazaleh, Plets David, Ben Van Herbruggen, Jaron Fontaine, Leen Verloock, Eli De Poorter, and Jukka Talvitie. 2023. UWB CIR Data Collected in 9 Different Environments in Ghent, Belgium. IEEE Dataport. DOI:
[36]
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. Advan. Neural Inf. Process. Syst. 27 (2014).
[37]
Z. Gu, H. Chen, P. Xu, Y. Li, and B. Vucetic. 2020. Physical layer authentication for non-coherent massive SIMO-enabled industrial IoT communications. IEEE Trans. Inf. Forens. Secur. 15 (2020), 3722–3733.
[38]
N. Gulati, R. Greenstadt, K. R. Dandekar, and J. M. Walsh. 2013. GMM based semi-supervised learning for channel-based authentication scheme. In IEEE Vehicular Technology Conference (VTC’13). 1–6.
[39]
J. Han, C. Qian, P. Yang, D. Ma, Z. Jiang, W. Xi, and J. Zhao. 2016. GenePrint: Generic and accurate physical-layer identification for UHF RFID tags. IEEE/ACM Trans. Netw. 24, 2 (2016), 846–858.
[40]
K. Hara, D. Saito, and H. Shouno. 2015. Analysis of function of rectified linear unit used in deep learning. In International Joint Conference on Neural Networks (IJCNN’15). 1–8.
[41]
Tiep M. Hoang, Alireza Vahid, Hoang Duong Tuan, and Lajos Hanzo. 2024. Physical layer authentication and security design in the machine learning era. IEEE Communications Surveys and Tutorials 26, 3 (2024), 1830–1860.
[42]
W. Hou, X. Wang, and J. Chouinard. 2012. Physical layer authentication in OFDM systems based on hypothesis testing of CFO estimates. In IEEE International Conference on Communications (ICC’12). 3559–3563.
[43]
W. Hou, X. Wang, J. Chouinard, and A. Refaey. 2014. Physical layer authentication for mobile systems with time-varying carrier frequency offsets. IEEE Trans. Commun. 62, 5 (2014), 1658–1667.
[44]
Elmehdi Illi, Marwa Qaraqe, Saud Althunibat, Abdullah Alhasanat, Moath Alsafasfeh, Marcus de Ree, Georgios Mantas, Jonathan Rodriguez, Waqas Aman, and Saif Al-Kuwari. 2024. Physical layer security for authentication, confidentiality, and malicious node detection: A paradigm shift in securing IoT networks. IEEE Communications Surveys and Tutorials 26, 1 (2024), 347–388.
[45]
Martin Jaggi. 2013. Revisiting Frank-Wolfe: Projection-free sparse convex optimization. In International Conference on Machine Learning (ICML’13). 427–435.
[46]
J. R. Jiang. 2020. Short survey on physical layer authentication by machine-learning for 5G-based internet of things. In IEEE International Conference on Knowledge Innovation and Invention (ICKII’20). 41–44.
[47]
Yang Jing, Ji Xinsheng, Huang Kaizhi, Chen Yajun, and Qi Xiaohui. 2015. A physical-layer authentication scheme based on hash method. In IEEE/CIC International Conference on Communications in China - Workshops (CIC/ICCC’15). 99–104.
[48]
Zhang Jinling, H. Wen, Song Huanhuan, Jiang Yixin, Zhang Zhengguang, Zhang Luping, and Zhu Xiping. 2016. Using basis expansion model for physical layer authentication in time-variant system. In IEEE Conference on Communications and Network Security (CNS’16). 348–349.
[49]
Li Junhong. 2009. Design of authentication protocols preventing replay attacks. In International Conference on Future BioMedical Information Engineering (FBIE’09). 362–365.
[50]
B. Kang, D. G. García, J. Lijffijt, R. Santos-Rodríguez, and T. de Bie. 2021. Conditional t-SNE: More informative t-SNE embeddings. In IEEE International Conference on Data Science and Advanced Analytics (DSAA’21). 1–2.
[51]
Kazuya Kawakami. 2008. Supervised Sequence Labelling with Recurrent Neural Networks. Ph. D. Dissertation. Technical University of Munich.
[52]
Steven M. Kay. 1998. Fundamentals of Statistical Signal Processing: Detection Theory. Prentice Hall PTR, NJ.
[53]
M. Khalid, R. Zhao, and N. Ahmed. 2020. Physical layer authentication in line-of-sight underwater acoustic sensor networks. In Global Oceans 2020: Singapore – U.S. Gulf Coast (OCEANS’20). 1–5.
[54]
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436–444.
[55]
W. Li, N. Wang, L. Jiao, and K. Zeng. 2021. Physical layer spoofing attack detection in mmWave massive MIMO 5G networks. IEEE Access 9 (2021), 60419–60432.
[56]
Xingwang Li, Qunshu Wang, Ming Zeng, Yuanwei Liu, Shuping Dang, Theodoros A. Tsiftsis, and Octavia A. Dobre. 2023. Physical-layer authentication for ambient backscatter-aided NOMA symbiotic systems. IEEE Trans. Commun. 71 (2023), 2288–2303.
[57]
R. Liao, H. Wen, F. Pan, H. Song, A. Xu, and Y. Jiang. 2019. A novel physical layer authentication method with convolutional neural network. In IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA’19). 231–235.
[58]
R. F. Liao, H. Wen, S. Chen, F. Xie, F. Pan, J. Tang, and H. Song. 2020. Multiuser physical layer authentication in internet of things with data augmentation. IEEE Internet Things J. 7, 3 (2020), 2077–2088.
[59]
Run-Fa Liao, Hong Wen, Jinsong Wu, Fei Pan, Aidong Xu, Yixin Jiang, Feiyi Xie, and Minggui Cao. 2019. Deep-learning-based physical layer authentication for industrial wireless sensor networks. Sensors 19, 11 (2019).
[60]
R. F. Liao, H. Wen, J. Wu, F. Pan, A. Xu, H. Song, F. Xie, Y. Jiang, and M. Cao. 2019. Security enhancement for mobile edge computing through physical layer authentication. IEEE Access 7 (2019), 116390–116401.
[61]
F. J. Liu, X. Wang, and S. L. Primak. 2013. A two dimensional quantization algorithm for CIR-based physical layer authentication. In IEEE International Conference on Communications (ICC’13). 4724–4728.
[62]
F. J. Liu, Wang Xianbin, and H. Tang. 2011. Robust physical layer authentication using inherent properties of channel impulse response. In Military Communications Conference (MILCOM’11).
[63]
Hongbo Liu, Yan Wang, Jian Liu, Jie Yang, and Yingying Chen. 2014. Practical user authentication leveraging channel state information (CSI). In ACM Symposium on Information, Computer, and Communications Security (ASIA CCS’14). 389–400.
[64]
Hongbo Liu, Wang Yan, Liu Jian, Yang Jie, Yingying Chen, and Vincent Poor. 2018. Authenticating users through fine-grained channel information. IEEE Trans. Mob. Comput. 17 (2018), 251–264.
[65]
J. Liu and X. Wang. 2016. Physical layer authentication enhancement using two-dimensional channel quantization. IEEE Trans. Wirel. Commun. 15, 6 (2016), 4171–4182.
[66]
Y. Liu, H. H. Chen, and L. Wang. 2017. Physical layer security for next generation wireless networks: Theories, technologies, and challenges. IEEE Commun. Surv. Tutor. 19, 1 (2017), 347–376.
[67]
Yangyang Liu, Pinchang Zhang, Jun Liu, Yulong Shen, and Xiaohong Jiang. 2023. Exploiting fine-grained channel/hardware features for PHY-layer authentication in MmWave MIMO systems. IEEE Trans. Inf. Forens. Secur. 18 (2023), 4059–4074.
[68]
Bingxian Lu, Zhenquan Qin, Mingyi Yang, Xu Xia, Renjie Zhang, and Lei Wang. 2018. Spoofing attack detection using physical layer information in cross-technology communication. In 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON’18). 1–2. DOI:
[69]
X. Lu, J. Lei, Y. Shi, and W. Li. 2022. Improved physical layer authentication scheme based on wireless channel phase. IEEE Wireless Commun. Lett. 11, 1 (2022), 198–202.
[70]
Larry R. Medsker and L. C. Jain. 2001. Recurrent neural networks. Des. Applic. 5, 64-67 (2001), 2.
[71]
Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. ArXiv abs/1411.1784 (2014).
[72]
Gabriele Oligeri, Savio Sciancalepore, Simone Raponi, and Roberto Di Pietro. 2023. PAST-AI: Physical-layer authentication of satellite transmitters via deep learning. IEEE Trans. Inf. Forens. Secur. 18 (2023), 274–289.
[73]
F. Pan, Z. Pang, M. Luvisotto, M. Xiao, and H. Wen. 2018. Physical-layer security for industrial wireless control systems: Basics and future directions. IEEE Industr. Electron. Mag. 12, 4 (2018), 18–27.
[74]
F. Pan, Z. Pang, H. Wen, M. Luvisotto, M. Xiao, R. Liao, and J. Chen. 2019. Threshold-free physical layer authentication based on machine learning for industrial wireless CPS. IEEE Trans. Industr. Inform. 15, 12 (2019), 6481–6491.
[75]
F. Pan, H. Wen, R. Liao, Y. Jiang, A. Xu, K. Ouyang, and X. Zhu. 2017. Physical layer authentication based on channel information and machine learning. In IEEE Conference on Communications and Network Security (CNS’17). 364–365.
[76]
C. Pei, N. Zhang, X. S. Shen, and J. W. Mark. 2014. Channel-based physical layer authentication. In IEEE Global Communications Conference (GLOBECOM’14). 4114–4119.
[77]
L. Peng, J. Zhang, M. Liu, and A. Hu. 2020. Deep learning based RF fingerprint identification using differential constellation trace figure. IEEE Trans. Vehic. Technol. 69, 1 (2020), 1091–1095.
[78]
X. Qiu, J. Dai, and M. Hayes. 2020. A learning approach for physical layer authentication using adaptive neural network. IEEE Access 8 (2020), 26139–26149.
[79]
X. Qiu, Z. Du, and X. Sun. 2019. Artificial intelligence-based security authentication: Applications in wireless multimedia networks. IEEE Access 7 (2019), 172004–172011.
[80]
X. Qiu, T. Jiang, S. Wu, and M. Hayes. 2018. Physical layer authentication enhancement using a Gaussian mixture model. IEEE Access 6 (2018), 53583–53592.
[81]
Richard A. Roberts and Clifford T. Mullis. 1987. Digital Signal Processing. Addison-Wesley Longman Co., MA.
[82]
Pieter Robyns, Eduard Marin, Wim Lamotte, Peter Quax, Dave Singelée, and Bart Preneel. 2017. Physical-layer fingerprinting of LoRa devices using supervised and zero-shot learning. In ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec’17). 58–63.
[83]
M. Rostaghi and H. Azami. 2016. Dispersion entropy: A measure for time-series analysis. IEEE Sig. Process. Lett. 23, 5 (2016), 610–614.
[84]
S. Rumpel, A. Wolf, and E. A. Jorswieck. 2016. Physical layer based authentication without phase detection. In Asilomar Conference on Signals, Systems and Computers (ACSSC’16). 1675–1679.
[85]
A. S. Saqlain, F. Fang, T. Ahmad, L. Wang, and Z. U. Abidin. 2021. Evolution and effectiveness of loss functions in generative adversarial networks. China Commun. 18, 10 (2021), 45–76.
[86]
Jürgen Schmidhuber. 2015. Deep learning in neural networks: An overview. Neural Netw. 61 (2015), 85–117.
[87]
L. Senigagliesi, M. Baldi, and E. Gambi. 2019. Statistical and machine learning-based decision techniques for physical layer authentication. In IEEE Global Communications Conference (GLOBECOM’19). 1–6.
[88]
L. Senigagliesi, M. Baldi, and E. Gambi. 2020. Physical layer authentication techniques based on machine learning with data compression. In IEEE Conference on Communications and Network Security (CNS’20). 1–6.
[89]
L. Senigagliesi, M. Baldi, and E. Gambi. 2021. Comparison of statistical and machine learning techniques for physical layer authentication. IEEE Trans. Inf. Forens. Secur. 16 (2021), 1506–1521.
[90]
L. Senigagliesi, M. Baldi, and E. Gambi. 2021. Physical layer authentication with cooperative wireless communications and machine learning. In IEEE Latin-American Conference on Communications (LATINCOM’21). 1–6.
[91]
L. Senigagliesi, L. Cintioni, M. Baldi, and E. Gambi. 2019. Blind physical layer authentication over fading wireless channels through machine learning. In IEEE International Workshop on Information Forensics and Security (WIFS’19). 1–6.
[92]
D. Shan, K. Zeng, W. Xiang, P. Richardson, and Y. Dong. 2013. PHY-CRAM: Physical layer challenge-response authentication mechanism for wireless networks. IEEE J. Select. Areas Commun. 31, 9 (2013), 1817–1827.
[93]
Karl J. Smith. 2013. Precalculus: A Functional Approach to Graphing and Problem Solving. Jones and Bartlett Learning, Burlington, USA.
[94]
R. S. Sutton and A. G. Barto. 1998. Reinforcement learning: An introduction. IEEE Trans. Neural Netw. 9, 5 (1998), 1054–1054.
[95]
Daniel Svozil, Vladimir Kvasnicka, and Jiri Pospichal. 1997. Introduction to multi-layer feed-forward neural networks. Chemomet. Intell. Lab. Syst. 39, 1 (1997), 43–62.
[96]
Jie Tang, Hong Wen, and Huanhuan Song. 2022. Physical layer authentication for 5G/6G millimeter wave communications by using channel sparsity. IET Commun. 16, 3 (2022), 206–217.
[97]
J. Tang, A. Xu, Y. Jiang, Y. Zhang, H. Wen, and T. Zhang. 2020. MmWave MIMO physical layer authentication by using channel sparsity. In IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS’20). 221–224.
[98]
Michel Tokic. 2010. Adaptive \(\varepsilon\)-greedy exploration in reinforcement learning based on value differences. In Annual German Conference on AI (KI’10). Springer, 203–210.
[99]
J. K. Tugnait and H. Kim. 2010. A channel-based hypothesis testing approach to enhance user authentication in wireless networks. In International Conference on Communication Systems and Networks (COMSNETS’10). 1–9.
[100]
Bill Waggener and William N. Waggener. 1995. Pulse Code Modulation Techniques. Springer Science and Business Media, Berlin, Germany.
[101]
X. Wan, L. Xiao, Q. Li, and Z. Han. 2017. PHY-layer authentication with multiple landmarks with reduced communication overhead. In IEEE International Conference on Communications (ICC’17). 1–6.
[102]
N. Wang, T. Jiang, S. Lv, and L. Xiao. 2017. Physical-layer authentication based on extreme learning machine. IEEE Commun. Lett. 21, 7 (2017), 1557–1560.
[103]
N. Wang, W. Li, P. Wang, A. Alipour-Fanid, L. Jiao, and K. Zeng. 2020. Physical layer authentication for 5G communications: Opportunities and road ahead. IEEE Netw. 34, 6 (2020), 198–204.
[104]
Q. Wang, H. Li, D. Zhao, Z. Chen, S. Ye, and J. Cai. 2019. Deep neural networks for CSI-based authentication. IEEE Access 7 (2019), 123026–123034.
[105]
Shaoyu Wang, Kaizhi Huang, Xiaoming Xu, Zhou Zhong, and You Zhou. 2022. CSI-based physical layer authentication via deep learning. IEEE Wireless Commun. Lett. 11 (2022), 1748–1752.
[106]
S. Wang, N. Li, S. Xia, X. Tao, and H. Lu. 2021. Collaborative physical layer authentication in internet of things based on federated learning. In IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC’21). 714–719.
[107]
Wei Wang, Xin Liu, Zicheng Chi, Stuart Ray, and Ting Zhu. 2024. Key establishment for secure asymmetric cross-technology communication. In 19th ACM Asia Conference on Computer and Communications Security. 412–422.
[108]
X. Wang, P. Hao, and L. Hanzo. 2016. Physical-layer authentication for wireless security enhancement: Current challenges and future developments. IEEE Commun. Mag. 54, 6 (2016), 152–158.
[109]
X. Wang, G. Wang, R. He, and Y. Zou. 2017. Uplink channel estimation with basis expansion model and expectation maximization for wireless communication systems on high speed railways. In International Conference on Communications in China (ICCC’17). 1–5.
[110]
Y. Wang, J. Jin, Y. Li, and C. Choi. 2020. A reliable physical layer authentication algorithm for massive IoT systems. IEEE Access 8 (2020), 80684–80690.
[111]
A. Weinand, M. Karrenbauer, J. Lianghai, and H. D. Schotten. 2017. Physical layer authentication for mission critical machine type communication using gaussian mixture model based clustering. In IEEE Vehicular Technology Conference (VTC Spring’17). 1–5.
[112]
A. Weinand, M. Karrenbauer, R. Sattiraju, and H. Schotten. 2017. Application of machine learning for channel based message authentication in mission critical machine type communication. In European Wireless Conference (EWC’17). 1–5.
[113]
X. Wu and Z. Yang. 2015. Physical-layer authentication for multi-carrier transmission. IEEE Commun. Lett. 19, 1 (2015), 74–77.
[114]
X. Wu, Z. Yang, C. Ling, and X. G. Xia. 2016. Artificial-noise-aided physical layer phase challenge-response authentication for practical OFDM transmission. IEEE Trans. Wirel. Commun. 15, 10 (2016), 6611–6625.
[115]
Yuemei Wu, Dong Wei, Caili Guo, and Weiqing Huang. 2023. Physical layer authentication based on channel polarization response in dual-polarized antenna communication systems. IEEE Trans. Inf. Forens. Secur. 18 (2023), 2144–2159.
[116]
S. Xia, X. Tao, N. Li, S. Wang, T. Sui, H. Wu, J. Xu, and Z. Han. 2021. Multiple correlated attributes based physical layer authentication in wireless networks. IEEE Trans. Vehic. Technol. 70, 2 (2021), 1673–1687.
[117]
L. Xiao, L. J. Greenstein, N. B. Mandayam, and W. Trappe. 2008. Using the physical layer for wireless authentication in time-variant channels. IEEE Trans. Wirel. Commun. 7, 7 (2008), 2571–2579.
[118]
L. Xiao, Y. Li, G. Han, G. Liu, and W. Zhuang. 2016. PHY-Layer spoofing detection with reinforcement learning in wireless networks. IEEE Trans. Vehic. Technol. 65, 12 (2016), 10037–10047.
[119]
L. Xiao, Y. Li, G. Liu, Q. Li, and W. Zhuang. 2015. Spoofing detection with reinforcement learning in wireless networks. In IEEE Global Communications Conference (GLOBECOM’15). 1–5.
[120]
L. Xiao, X. Lu, T. Xu, W. Zhuang, and H. Dai. 2021. Reinforcement learning-based physical-layer authentication for controller area networks. IEEE Trans. Inf. Forens. Secur. 16 (2021), 2535–2547.
[121]
L. Xiao, G. Sheng, X. Wan, W. Su, and P. Cheng. 2019. Learning-based PHY-layer authentication for underwater sensor networks. IEEE Commun. Lett. 23, 1 (2019), 60–63.
[122]
L. Xiao, X. Wan, and Z. Han. 2018. PHY-layer authentication with multiple landmarks with reduced overhead. IEEE Trans. Wirel. Commun. 17, 3 (2018), 1676–1687.
[123]
Feiyi Xie, Zhibo Pang, Hong Wen, Wenxin Lei, and Xinchen Xu. 2021. Weighted voting in physical layer authentication for industrial wireless edge networks. IEEE Transactions on Industrial Informatics 18, 4 (2021), 2796–2806.
[124]
N. Xie, C. Chen, and Z. Ming. 2021. Security model of authentication at the physical layer and performance analysis over fading channels. IEEE Trans. Depend. Sec. Comput. 18, 1 (2021), 253–268.
[125]
N. Xie, J. Chen, and L. Huang. 2021. Physical-layer authentication using multiple channel-based features. IEEE Trans. Inf. Forens. Secur. 16 (2021), 2356–2366.
[126]
N. Xie, Z. Li, and H. Tan. 2021. A survey of physical-layer authentication in wireless communications. IEEE Commun. Surv. Tutor. 23, 1 (2021), 282–310.
[127]
N. Xie, W. Wang, Y. Chen, P. Zhang, and L. Huang. 2022. Confidentiality-preserving edge-based wireless communications for contact tracing systems. IEEE J. Select. Areas Commun. 40, 11 (2022), 3152–3171.
[128]
N. Xie, W. Xiong, J. Chen, P. Zhang, L. Huang, and J. Su. 2022. Multiple phase noises physical-layer authentication. IEEE Trans. Commun. 70, 9 (2022), 6196–6211.
[129]
Ning Xie, Jiaheng Zhang, Qihong Zhang, Haijun Tan, Alex X. Liu, and Dusit Niyato. 2023. Hybrid physical-layer authentication. IEEE Transactions on Mobile Computing 23, 2 (2023), 1295–1311.
[130]
Ning Xie, Qihong Zhang, Junjie Chen, and Haijun Tan. 2022. Privacy-preserving physical-layer authentication for non-orthogonal multiple access systems. IEEE J. Select. Areas Commun. 40 (2022), 1371–1385.
[131]
Ning Xie, Shengli Zhang, and Alex X. Liu. 2020. Physical-layer authentication in non-orthogonal multiple access systems. IEEE/ACM Trans. Netw. 28 (2020), 1144–1157.
[132]
Q. Xu, R. Zheng, W. Saad, and Z. Han. 2016. Device fingerprinting in wireless networks: Challenges and opportunities. IEEE Commun. Surv. Tutor. 18, 1 (2016), 94–104.
[133]
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019. Federated machine learning: Concept and applications. ACM Trans. Intell. Syst. Technol. 10, 2 (2019), Article 12.
[134]
J. Yoon, Y. Lee, and E. Hwang. 2019. Machine learning-based physical layer authentication using neighborhood component analysis in MIMO wireless communications. In International Conference on Information and Communication Technology Convergence (ICTC’19). 63–65.
[135]
P. L. Yu, J. S. Baras, and B. M. Sadler. 2008. Physical-layer authentication. IEEE Trans. Inf. Forens. Secur. 3, 1 (2008), 38–51.
[136]
Sihan Yu, Xiaonan Zhang, Pei Huang, and Linke Guo. 2019. Secure authentication in cross-technology communication for heterogeneous IoT. In IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN’19). 1–2. DOI:
[137]
Sihan Yu, Xiaonan Zhang, Pei Huang, Linke Guo, Long Cheng, and Kuangching Wang. 2020. AuthCTC: Defending against waveform emulation attack in heterogeneous IoT environments. In 15th ACM Asia Conference on Computer and Communications Security. 20–32.
[138]
P. Zhang, J. Liu, Y. Shen, and X. Jiang. 2021. Exploiting channel gain and phase noise for PHY-layer authentication in massive MIMO systems. IEEE Trans. Inf. Forens. Secur. 16 (2021), 4265–4279.
[139]
P. Zhang, Y. Shen, X. Jiang, and B. Wu. 2020. Physical layer authentication jointly utilizing channel and phase noise in MIMO systems. IEEE Trans. Commun. 68, 4 (2020), 2446–2458.
[140]
P. Zhang, J. Zhu, Y. Chen, and X. Jiang. 2019. End-to-end physical layer authentication for dual-hop wireless networks. IEEE Access 7 (2019), 38322–38336.
[141]
Xiaonan Zhang, Sihan Yu, Hansong Zhou, Pei Huang, Linke Guo, and Ming Li. 2023. Signal emulation attack and defense for smart home IoT. IEEE Trans. Depend. Sec. Comput. 20, 3 (2023), 2040–2057. DOI:
[142]
Y. Zhang. 2021. Physical layer authentication based on Gaussian mixture model under unknown number of attackers. In IEEE/CIC International Conference on Communications in China (ICCC’21). 70–74.
[143]
Z. Zhang, N. Li, S. Xia, and X. Tao. 2020. Fast cross layer authentication scheme for dynamic wireless network. In IEEE Wireless Communications and Networking Conference (WCNC’20). 1–6.
[144]
Caidan Zhao, Minmin Huang, Lianfen Huang, Xiaojiang Du, and Mohsen Guizani. 2017. A robust authentication scheme based on physical-layer phase noise fingerprint for emerging wireless networks. Comput. Netw. 128 (2017), 164–171.
Index Terms
- A Comprehensive Survey on Physical Layer Authentication Techniques: Categorization and Analysis of Model-Driven and Data-Driven Approaches
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Publication History
Published: 09 January 2025
Online AM: 16 December 2024
Accepted: 11 November 2024
Revised: 03 November 2024
Received: 29 April 2024
Published in CSUR Volume 57, Issue 5
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- Natural Science Foundation of China
- Natural Science Foundation of Guangdong, China
- Key Project of Education Ministry of Guangdong Province
- Fundamental Research Programs of Shenzhen City
- National Research Foundation, Singapore, and Infocomm Media Development Authority under its Future Communications Research & Development Programme, Defence Science Organisation (DSO) National Laboratories under the AI Singapore Programme
- Singapore Ministry of Education (MOE) Tier 1
- NTU Centre for Computational Technologies in Finance
- Seitee Pte Ltd, and China Scholarship Council
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