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
Creative Commons License
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 License.
Comments
This is an Author's Accepted Manuscript of an article published by IEEE. © 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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