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

Authors: Hoang, T.M., Liu, D., Van Luong, T., Zhang, J. and Hanzo, L.

Journal: IEEE Transactions on Cognitive Communications and Networking

eISSN: 2332-7731

DOI: 10.1109/TCCN.2021.3138392

Abstract:

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.

http://eprints.bournemouth.ac.uk/36412/

Source: Scopus

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

Authors: Hoang, T.M., Liu, D., Luong, T.V., Zhang, J. and Hanzo, L.

Journal: IEEE Transactions on Cognitive Communications and Networking

Publisher: Institute of Electrical and Electronics Engineers

ISSN: 2332-7731

http://eprints.bournemouth.ac.uk/36412/

Source: Manual