DRL-enabled secure uplink transmission for MIMO-OFDM IoT systems against intelligent eavesdroppers✩

Yingzhen Wu; Yan Huo; Xin Fan; Qinghe Gao; Jian Mao; Tao Jing

doi:10.1016/j.dcan.2025.08.004

›› 2026, Vol. 12 ›› Issue (3) :528 -529. DOI: 10.1016/j.dcan.2025.08.004

Research Article

research-article

DRL-enabled secure uplink transmission for MIMO-OFDM IoT systems against intelligent eavesdroppers^✩

Yingzhen Wu ^a
, Yan Huo ^a^,^*
, Xin Fan ^b
, Qinghe Gao ^a
, Jian Mao ^c^,^d^,^e^,^f
, Tao Jing ^a

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^a School of Electronics and Information Engineering, Beijing Jiaotong University , Beijing 100044, China

^b School of Information Science and Technology, Beijing Forestry University , Beijing 100083, China

^c School of Cyber Science and Technology, Beihang University , Beijing 100191, China

^d Hangzhou Innovation Institute, Beihang University , Hangzhou 310000, China

^e Tianmushan Laboratory, Beihang University , Hangzhou 311121, China

^f Zhongguancun Laboratory , Beijing 100094, China

^* E-mail addresses: yingzhenwu@bjtu.edu.cn (Y. Wu),

yhuo@bjtu.edu.cn (Y. Huo),

fanxin@bjfu.edu.cn (X. Fan),

qhgao@bjfu.edu.cn (Q. Gao),

maojian@buaa.edu.cn (J. Mao),

tjing@bjtu.edu.cn (T. Jing).

Yingzhen Wu received the B.S. degree from North China Electric Power University, Baoding, in 2019. He is currently pursuing the Ph.D.degree in information and communication engineering with Beijing Jiaotong University. His current research interests include physical layer security, the Internet of Things and machine learning.

Yan Huo received the B.E. and Ph.D. degrees in communication and information system from Beijing Jiaotong University, Beijing, China, in 2004 and 2009, respectively. He was a Visiting Scholar with the Department of Computer Science, George Washington University, from 2015 to 2016. He is currently a Professor with the School of Electronics and Information Engineering, Beijing Jiaotong University. His current research focuses on wireless communications, security and privacy, and the Internet of Things. It involves building and simulating prototype systems and conducting real experiments and measurements.

Xin Fan received the B.S., M.E. and Ph.D. degrees from Beijing Jiaotong University. He was a Visiting Scholar with the Electrical and Computer Engineering Department, George Mason University, Fairfax, VA, USA, from 2020 to 2022. He is currently an Assistant Professor at Beijing Forestry University. His current research interests lie in the areas of signal processing, communications, and machine learning.

Qinghe Gao received the B.E. degree in communication engineering from North China Electric Power University, Baoding, China, in 2013, and the Ph.D. degree in communication and information system from Beijing Jiaotong University, Beijing, China, in 2020. She was a Visiting Scholar with the Department of Computer Science, George Washington University, Washington, DC, USA, from 2015 to 2017. She is currently a Faculty Member with the School of Electronics and Information Engineering, Beijing Jiaotong University. Her research interests include physical layer security, energy efficient resource allocation schemes, energy harvesting, Internet of Things (IoT), and Industrial IoT.

Jian Mao received the B.S. and Ph.D. degrees from Xidian University, Xi’an, China, in 1997 and 2004. She is currently a Professor with the School of Cyber Science and Technology, Beihang University, Beijing, China. Her research interests include cloud security, IoT security, Web security, and mobile security.

Tao Jing received his Bachelor of Engineering degree in information engineering from Electronic technology department of national university of defense technology in 1991, and his Master of Engineering degree from Chang Chun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences in 1994. Then he got his Doctor of Engineering degree from Chang Chun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences in 1999, and graduated from postdoctoral research station of Beijing Jiaotong University in 2011. Now he mainly researches on Network Security and Wireless Broadband Communication and Resource Management.

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Abstract

Physical layer security has emerged as a promising approach to counter eavesdropping threats in the Internet of Things (IoT). With the advancement of technology, eavesdroppers (Eves) are increasingly sophisticated, exacerbating the difficulties in ensuring the security of uplink transmissions. In situations where transmitters are unable to deter Eves from approaching at close range, these intelligent Eves can attain substantial channel gains, thereby posing significant security threats. To address these challenges, we first propose a time-domain artificial noise (AN) scheme to eliminate interference at the legitimate receiver in Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems. Then, we model the adversarial interaction between legitimate transceivers and intelligent Eves as a game-theoretic problem. To solve the dynamic game problem, we introduce a deep deterministic policy gradient (DDPG)-based framework, where an optimal beamforming fractional programming algorithm (OBFP) and a lower-complexity zero-forcing beamforming difference of convex algorithm (ZFDC) are designed to ascertain the most effective strategy for legitimate transceivers. Simulation results validate the effectiveness of our proposed algorithms and highlight the critical role of time-domain AN in combating intelligent eavesdropping.

Keywords

Physical layer security / IoT / Intelligent eavesdroppers / Time-domain artificial noise / Convex optimization / Deep reinforcement learning

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Yingzhen Wu, Yan Huo, Xin Fan, Qinghe Gao, Jian Mao, Tao Jing. DRL-enabled secure uplink transmission for MIMO-OFDM IoT systems against intelligent eavesdroppers^✩. , 2026, 12 (3) : 528-529 DOI:10.1016/j.dcan.2025.08.004

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CRediT authorship contribution statement

Yingzhen Wu: Writing -- original draft, Methodology, Investigation, Conceptualization. Yan Huo: Writing -- review & editing, Supervision, Investigation, Funding acquisition, Conceptualization. Xin Fan: Writing -- review & editing, Investigation. Qinghe Gao: Writing -- review & editing, Funding acquisition, Conceptualization. Jian Mao: Validation, Supervision, Funding acquisition. Tao Jing: Supervision, Funding acquisition.

Declaration of competing interest

The authors declared that they have no conflicts of interest to this work.

Acknowledgements

This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant 2019JBZ001, in part by the National Natural Science Foundation of China under Grant U24B20117, Grant 62172027, Grant 62202035, and Grant U2368202, in part by the China Postdoctoral Science Foundation under Grant 2024M750199, and in part by the Natural Science Foundation of Zhejiang Province of China under Grant LZ23F020013.

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	X. Chen, W. Xu, Y. Wang, D.K.Y. Yau, Y. Li, Z. Cai, Distributed sparse signal detection with energy—efficient censoring—quantization in wireless sensor networks, IEEE Trans. Green Commun. Netw. 9 (3) (2025) 1079-1091.

[2]	H. Xu, Z. Cai, R. Li, W. Li, Efficient CityCam—to—edge cooperative learning for vehicle counting in ITS, IEEE Trans. Intell. Transp. Syst. 23 (9) (2022) 16600-16611.

[3]	J. Yu, H. Yao, K. Ouyang, X. Cao, L. Zhang, BPS—FL: blockchain—based privacy—preserving and secure federated learning, Big Data Min. Anal. 8 (1) (2025) 189-213.

[4]	P. Dong, N. Hou, Y. Tang, Y. Cheng, X. Ji, Device authentication for 5G terminals via radio frequency fingerprints, High—Confid. Comput. 4 (4) (2024) 100222.

[5]	C. Hazman, A. Guezzaz, S. Benkirane, M. Azrour, Enhanced IDS with deep learning for IoT—based smart cities security, Tsinghua Sci. Technol. 29 (4) (2024) 929-947.

[6]	M. Li, P. Xue, H. Yuan, Y. Han, Physical layer security for CR—NOMA network with cooperative jamming, Tsinghua Sci. Technol. 30 (2) (2025) 708-720.

[7]	J. Qiu, K. Xu, X. Xia, Z. Shen, W. Xie, D. Zhang, M. Wang, Secure transmission scheme based on fingerprint positioning in cell—free massive MIMO systems, IEEE Trans. Signal Inf. Process. Netw. 8 (2022) 92-105.

[8]	C. Wang, H.—M. Wang, Physical layer security in millimeter wave cellular networks, IEEE Trans. Wirel. Commun. 15 (8) (2016) 5569-5585.

[9]	N. Zhao, F.R. Yu, Y. Chen, V.C.M. Leung, Collusive eavesdropping in interference alignment based wireless networks, IEEE Trans. Wirel. Commun. 16 (8) (2017) 5549-5561.

[10]	Y. Huo, X. Fan, L. Ma, X. Cheng, Z. Tian, D. Chen, Secure communications in tiered 5G wireless networks with cooperative jamming, IEEE Trans. Wirel. Commun. 18 (6) (2019) 3265-3280.

[11]	Y. Kwon, X. Wang, T. Hwang, A game with randomly distributed eavesdroppers in wireless ad hoc networks: a secrecy EE perspective, IEEE Trans. Veh. Technol. 66 (11) (2017) 9916-9930.

[12]	G. Su, M. Dai, B. Chen, X. Lin, H. Wang, Secrecy—oriented user association in ultra dense heterogeneous networks against strategically colluding adversaries, IET Commun. 16 (6) (2022) 695-709.

[13]	L.P. Rachakonda, M. Siddula, V. Sathya, A comprehensive study on IoT privacy and security challenges with focus on spectrum sharing in next—generation networks (5G/6G/Beyond), High—Confid. Comput. 4 (2) (2024) 100220.

[14]	H. Huang, Y. Huo, R. Li, Q. Gao, Y. Wu, Z. Yang, A soft—handoff—based cooperative jamming scheme for security in mobility scenarios, IEEE Trans. Commun. 72 (12) (2024) 7894-7906.

[15]	D. Shen, Y. Huo, Q. Gao, A friendly jamming handover scheme for a conscious mobile eavesdropper in IoT systems, in: International Conference on Identification, Information and Knowledge in the Internet of Things, 2021, Proc. Comput. Sci. 202 (2022) 15-20.

[16]	H. Wu, M. Li, Q. Gao, Z. Wei, N. Zhang, X. Tao, Eavesdropping and anti—eavesdropping game in UAV wiretap system: a differential game approach, IEEE Trans. Wirel. Commun. 21 (11) (2022) 9906-9920.

[17]	Y. Wu, Y. Huo, Q. Gao, Y. Wu, X. Li, Game—theoretic and learning—aided physical layer security for multiple intelligent eavesdroppers, in: 2022 IEEE Globecom Workshops (GC Wkshps), 2022, pp. 233-238.

[18]	C. Wen, Y. Fang, L. Qiu, Securing UAV communication based on multi—agent deep reinforcement learning in the presence of smart UAV eavesdropper, in: 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022, pp. 1164-1169.

[19]	H. Wu, H. Li, Z. Wei, N. Zhang, X. Tao, Secrecy performance analysis of air—to—ground communication with UAV jitter and multiple random walking eavesdroppers, IEEE Trans. Veh. Technol. 70 (1) (2021) 572-584.

[20]	P.C. Pinto, J. Barros, M.Z. Win, Secure communication in stochastic wireless networks—part I: connectivity, IEEE Trans. Inf. Forensics Secur. 7 (1) (2012) 125-138.

[21]	N. Romero—Zurita, D. McLernon, M. Ghogho, A. Swami, PHY layer security based on protected zone and artificial noise, IEEE Signal Process. Lett. 20 (5) (2013) 487-490.

[22]	M. Zhang, Y. Liu, R. Zhang, Artificial noise aided secrecy information and power transfer in OFDMA systems, IEEE Trans. Wirel. Commun. 15 (4) (2016) 3085-3096.

[23]	H. Qin, Y. Sun, T.—H. Chang, X. Chen, C.—Y. Chi, M. Zhao, J. Wang, Power allocation and time—domain artificial noise design for wiretap OFDM with discrete inputs, IEEE Trans. Wirel. Commun. 12 (6) (2013) 2717-2729.

[24]	T. Akitaya, S. Asano, T. Saba, Time—domain artificial noise generation technique using time—domain and frequency—domain processing for physical layer security in MIMO—OFDM systems, in: 2014 IEEE International Conference on Communications Workshops (ICC), 2014, pp. 807-812.

[25]	S. Li, N. Li, X. Tao, Z. Liu, H. Wang, J. Xu, Artificial noise inserted secure communication in time—reversal systems, in: 2018 IEEE Wireless Communications and Networking Conference (WCNC), 2018, pp. 1-6.

[26]	C. Ye, Z. Li, T. Jiang, C. Ni, Q. Qi, PAPR reduction of OQAM—OFDM signals using segmental PTS scheme with low complexity, IEEE Trans. Broadcast. 60 (1) (2014) 141-147.

[27]	D. Chen, Y. Tian, D. Qu, T. Jiang, OQAM—OFDM for wireless communications in future Internet of things: a survey on key technologies and challenges, IEEE Internet Things J. 5 (5) (2018) 3788-3809.

[28]	B.M. Lee, Energy efficient selected mapping schemes based on antenna grouping for industrial massive MIMO—OFDM antenna systems, IEEE Trans. Ind. Inform. 14 (11) (2018) 4804-4814.

[29]	S. Saravanan, U.M. Balasubramanian, An adaptive scalable data pipeline for multiclass attack classification in large—scale IoT networks, Big Data Min. Anal. 7 (2) (2024) 500-511.

[30]	Z. Xiong, Z. Cai, C. Hu, D. Takabi, W. Li, Towards neural network—based communication system: attack and defense, IEEE Trans. Dependable Secure Comput. 20 (4) (2023) 3238-3250.

[31]	N. Soltani, H. Cheng, M. Belgiovine, Y. Li, H. Li, B. Azari, S. D’Oro, T. Imbiriba, T. Melodia, P. Closas, Y. Wang, D. Erdogmus, K. Chowdhury, Neural network—based OFDM receiver for resource constrained IoT devices, IEEE Internet Things Mag. 5 (3) (2022) 158-164.

[32]	Z. Gao, M. Wu, C. Hu, F. Gao, G. Wen, D. Zheng, J. Zhang, Data—driven deep learning based hybrid beamforming for aerial massive MIMO—OFDM systems with implicit CSI, IEEE J. Sel. Areas Commun. 40 (10) (2022) 2894-2913.

[33]	H. Noh, H. Lee, H.J. Yang, Joint optimization on uplink OFDMA and MU—MIMO for IEEE 802.11ax: deep hierarchical reinforcement learning approach, IEEE Commun. Lett. 28 (8) (2024) 1800-1804.

[34]	Z. Wang, G. Giannakis, Wireless multicarrier communications, IEEE Signal Process. Mag. 17 (3) (2000) 29-48.

[35]	H. Jeon, N. Kim, J. Choi, H. Lee, J. Ha, Bounds on secrecy capacity over correlated ergodic fading channels at high SNR, IEEE Trans. Inf. Theory 57 (4) (2011) 1975-1983.

[36]	K. Shen, W. Yu, Fractional programming for communication systems—part I: power control and beamforming, IEEE Trans. Signal Process. 66 (10) (2018) 2616-2630.

[37]	B. Xie, Q. Zhang, J. Qin, Joint optimization of cooperative communication and computation in two—way relay MEC systems, IEEE Trans. Veh. Technol. 69 (4) (2020) 4596-4600.

[38]	S. Boyd, L. Vandenberghe, Convex Optimization, Cambridge University Press, Cambridge, UK, 2004.

[39]	H. Zhu, H. Qian, Optimal structure of receive beamforming for over—the—air computation, in: ICASSP 2024 — 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024, pp. 9206-9210.

[40]	G. Calcev, D. Chizhik, B. Goransson, S. Howard, H. Huang, A. Kogiantis, A.F. Molisch, A.L. Moustakas, D. Reed, H. Xu, A wideband spatial channel model for system—wide simulations, IEEE Trans. Veh. Technol. 56 (2) (2007) 389-403.