Intelligent Reflecting Surface Aided Secure Communication with Federated Learning
DOI:
https://doi.org/10.4108/eetmca.v7i4.2900Keywords:
Secure communication, federated learning, secrey outage probabilityAbstract
Applying federated learning into the covert communication can not only ensure the communication reliability, but also reduce the probability of enemy detection. The use of airspace resources is an effective way to achieve covert communication. However, most of the existing works on the airspace covert communication represented by MIMO need to adapt to the channel state and cannot improve the channel, resulting in the performance bottleneck of covert communication. The intelligent reflective surface (IRS) provides a new perspective for the covert communication by flexibly adjusting the reflection phase shift of the incident signal and intelligently configuring the wireless channel. However, the potential of IRS in the covert communication is far from being fully exploited. To solve this issue, this paper firstly performs a comprehensive literature review on the secure communication with the aid of federated learning, and then gives some challenges on the secure communication in poor channel state. In further, this paper provides some solutions to the challenges on the secure communication, where some results are provided to show the advantages. The research results have important theoretical and practical significance for forming a new research paradigm of airspace intelligent and controllable covert communication and promoting the application and popularization of covert communication in various fields of security.
Metrics
References
J. Lu, S. Lai, J. Xia, M. Tang, C. Fan, J. Ou, and D. Fan, “Performance analysis for irs-assisted mec networks with unit selection,” Physical Communication, vol. 55, p. 101869, 2022.
L. Zhang, S. Lai, J. Xia, C. Gao, D. Fan, and J. Ou, “Deep reinforcement learning based irs-assisted mobile edge computing under physical-layer security,” Physical Communication, p. 101896, 2022.
Y. Wu, J. Xia, C. Gao, J. Ou, C. Fan, J. Ou, and D. Fan, “Task offloading for vehicular edge computing with imperfect csi: A deep reinforcement approach,” Physical Communication, vol. 55, p. 101867, 2022.
X. Hu, C. Zhong, Y. Zhu, X. Chen, and Z. Zhang, “Programmable metasurface-based multicast systems: Design and analysis,” IEEE J. Sel. Areas Commun., vol. 38, no. 8, pp. 1763–1776, 2020.
X. Hu, J. Wang, and C. Zhong, “Statistical CSI based design for intelligent reflecting surface assisted MISO systems,” Science China: Information Science, vol. 63, no. 12, p. 222303, 2020.
D. Cai, P. Fan, Q. Zou, Y. Xu, Z. Ding, and Z. Liu, “Active device detection and performance analysis of massive non-orthogonal transmissions in cellular internet of things,” Science China information sciences, vol. 5, no. 8, pp. 182 301:1–182 301:18, 2022.
B. Wang, F. Gao, S. Jin, H. Lin, and G. Y. Li, “Spatial- and frequency-wideband effects in millimeter-wave massive MIMO systems,” IEEE Trans. Signal Processing, vol. 66, no. 13, pp. 3393–3406, 2018.
H. Ren, T. Huang, and H. Yan, “Adversarial examples: attacks and defenses in the physical world,” International Journal of Machine Learning and Cybernetics, vol. 12, no. 11, pp. 3325–3336, 2021.
K. Mo, W. Tang, J. Li, and X. Yuan, “Attacking deep reinforcement learning with decoupled adversarial policy,” IEEE Transactions on Dependable and Secure Computing, 2022.
H. Yan, L. Hu, X. Xiang, Z. Liu, and X. Yuan, “Ppcl: Privacy-preserving collaborative learning for mitigating indirect information leakage,” Information Sciences, vol. 548, pp. 423–437, 2021.
S. Sheikhzadeh, M. Pourghasemian, M. R. Javan, N. Mokari, and E. A. Jorswieck, “Ai-based secure NOMA and cognitive radio-enabled green communications: Channel state information and battery value uncertainties,” IEEE Trans. Green Commun. Netw., vol. 6, no. 2, pp. 1037–1054, 2022. [Online]. Available: https://doi.org/10.1109/TGCN.2021.3135479
X. Liu, C. Li, S. S. Ge, and D. Li, “Time-synchronized control of chaotic systems in secure communication,” IEEE Trans. Circuits Syst. I Regul. Pap., vol. 69, no. 9, pp. 3748–3761, 2022. [Online]. Available: https://doi.org/10.1109/TCSI.2022.3175713
Z. Cao, X. Ji, J. Wang, W. Wang, K. Cumanan, Z. Ding, and O. A. Dobre, “Artificial noise aided secure communications for cooperative NOMA networks,” IEEE Trans. Cogn. Commun. Netw., vol. 8, no. 2, pp. 946–963, 2022. [Online]. Available: https://doi.org/10.1109/TCCN.2021.3130979
G. Sun, J. Li, A. Wang, Q. Wu, Z. Sun, and Y. Liu, “Secure and energy-efficient UAV relay communications exploiting collaborative beamforming,” IEEE Trans. Commun., vol. 70, no. 8, pp. 5401–5416, 2022.[Online]. Available: https://doi.org/10.1109/TCOMM. 2022.3184160
L. Xiao, H. Li, S. Yu, Y. Zhang, L. Wang, and S. Ma, “Reinforcement learning based network coding for drone-aided secure wireless communications,” IEEE Trans. Commun., vol. 70, no. 9, pp. 5975–5988, 2022.[Online]. Available: https://doi.org/10.1109/TCOMM. 2022.3194074
M. Stute, F. Kohnhäuser, L. Baumgärtner, L. Almon, M. Hollick, S. Katzenbeisser, and B. Freisleben, “RESCUE: A resilient and secure device-to-device communication framework for emergencies,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 3, pp. 1722–1734, 2022. [Online]. Available: https: //doi.org/10.1109/TDSC.2020.3036224
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 EAI Endorsed Transactions on Mobile Communications and Applications
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
