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Deep Learning Aided Physical-Layer Security: The Security versus Reliability Trade-off.

Hoang, T.M., Liu, D., Luong, T.V., Zhang, J. and Hanzo, L., 2022. Deep Learning Aided Physical-Layer Security: The Security versus Reliability Trade-off. IEEE Transactions on Cognitive Communications and Networking, 8 (2), 442-453.

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2021-CAMERA_READY_TCCN_accepted_version.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1109/TCCN.2021.3138392


This paper considers a communication system whose source can learn from channel-related data, thereby making a suitable choice of system parameters for security improvement. The security of the communication system is optimized using deep neural networks (DNNs). More explicitly, the associated security vs reliability trade-off problem is characterized in terms of the symbol error probabilities and the discrete-input continuous-output memoryless channel (DCMC) capacities. A pair of loss functions were defined by relying on the Lagrangian and on the monotonic-function based techniques. These were then used for managing the learning/training process of the DNNs for finding near-optimal solutions to the associated non-convex problem. The Lagrangian technique was shown to approach the performance of the exhaustive search. We concluded by characterizing the security vs reliability trade-off in terms of the intercept probability vs the outage probability.

Item Type:Article
Additional Information:L. Hanzo would like to acknowledge the financial support of the Engineering and Physical Sciences Research Council projects EPP0342841 and EPP0039901 COALESCE as well as of the European Research Councils Advanced Fellow Grant QuantCom Grant No. 789028
Uncontrolled Keywords:Physical layer security, reliability, deep learning, neural network, Lagrange
Group:Faculty of Science & Technology
ID Code:36412
Deposited By: Symplectic RT2
Deposited On:04 Jan 2022 09:42
Last Modified:14 Jun 2022 15:33


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