# Graph Neural Networks for Physical-Layer Security in Multi-User Flexible-Duplex Networks

arXiv: https://arxiv.org/abs/2402.05378 · HTML: https://mlsanity.com/paper/2402.05378/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 7 of 7)

- [Optimal Wireless Resource Allocation with Random Edge Graph Neural Networks](https://mlsanity.com/paper/1909.01865/) (2019)
- [Link Scheduling using Graph Neural Networks](https://mlsanity.com/paper/2109.05536/) (2021)
- [Decentralized Inference with Graph Neural Networks in Wireless Communication Systems](https://mlsanity.com/paper/2104.09027/) (2021)
- [Customized Slicing for 6G: Enforcing Artificial Intelligence on Resource Management](https://mlsanity.com/paper/2102.10498/) (2021)
- [Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex Networks](https://mlsanity.com/paper/2301.11166/) (2023)
- [Game-Theoretic Mode Scheduling for Dynamic TDD in 5G Systems](https://mlsanity.com/paper/2104.12367/) (2021)
- [Physical-Layer Security for Untrusted UAV-Assisted Full-Duplex Wireless Networks](https://mlsanity.com/paper/1909.06600/) (2019)

## Abstract (arXiv)

This paper explores Physical-Layer Security (PLS) in Flexible Duplex (FlexD) networks, considering scenarios involving eavesdroppers. Our investigation revolves around the intricacies of the sum secrecy rate maximization problem, particularly when faced with coordinated and distributed eavesdroppers employing a Minimum Mean Square Error (MMSE) receiver. Our contributions include an iterative classical optimization solution and an unsupervised learning strategy based on Graph Neural Networks (GNNs). To the best of our knowledge, this work marks the initial exploration of GNNs for PLS applications. Additionally, we extend the GNN approach to address the absence of eavesdroppers' channel knowledge. Extensive numerical simulations highlight FlexD's superiority over Half-Duplex (HD) communications and the GNN approach's superiority over the classical method in both performance and time complexity.