In physical layer security, one interest of the community is the development of practical approaches to achieve reliable and secure communication, such as model-free approaches, in which the gradient of the channel model is required. This paper proposes a new model-free approach based on information-theoretic metrics. We train the encoder with deep reinforcement learning that uses a policy-based gradient descent algorithm whose loss function contains a feed-forward neural network. Simulation results show that our model is capable of retaining the eavesdropper’s BLER at a high level whilst ensuring the legitimate receiver’s BLER reduces to nearly zero.
Deep Reinforcement Learning For Secure Communication
01.09.2022
379283 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Joint optimization via deep reinforcement learning for secure-driven NOMA-UAV networks
Elsevier | 2025
|Coordination and communication in deep multi-agent reinforcement learning
BASE | 2022
|