Reinforcement learning-based secure communications over MIMO interference channels

Author Identifier (ORCID)

Shihao Yan: https://orcid.org/0000-0002-4586-1926

Abstract

This paper proposes a reinforcement learning-based precoding scheme with artificial noise to enhance secure communication in multi-input multi-output (MIMO) interference channel networks. The system consists of $K$ transmitter-receiver pairs communicating while exposed to a multi-antenna eavesdropper under channel uncertainty. To address the secrecy rate maximization problem, which involves highly non-convex optimization due to power constraints and coupled variables, the problem is formulated as a Markov decision process (MDP) and solved using the deep deterministic policy gradient (DDPG) algorithm. Numerical results show that the proposed approach achieves comparable secrecy performance to the latest asynchronous distributed pricing-based scheme while significantly reducing the computational complexity.

Document Type

Journal Article

Date of Publication

1-1-2025

Publication Title

IEEE Transactions on Vehicular Technology

Publisher

IEEE

School

School of Science

Comments

Wang, M., Kong, Z., Liu, S., Huang, T., Yan, S., Allahbakhsh, M., & Yuan, J. (2025). Reinforcement learning-based secure communications over MIMO interference channels. IEEE Transactions on Vehicular Technology. Advance online publication. https://doi.org/10.1109/TVT.2025.3608774

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Link to publisher version (DOI)

10.1109/TVT.2025.3608774