# Graph Neural Network-Based Bandwidth Allocation for Secure Wireless Communications

arXiv: https://arxiv.org/abs/2312.14958 · HTML: https://mlsanity.com/paper/2312.14958/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 13 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis](https://mlsanity.com/paper/2007.07632/) (2020)
- [Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin](https://mlsanity.com/paper/1907.01523/) (2019)
- [Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G](https://mlsanity.com/paper/2004.00507/) (2020)
- [Resource Allocation for Secure URLLC in Mission-Critical IoT Scenario](https://mlsanity.com/paper/1911.13154/) (2019)
- [Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?](https://mlsanity.com/paper/2207.06918/) (2022)

## Built on by (most cited first, 1 of 1)

- [Hybrid-Task Meta-Learning: A GNN Approach for Scalable and Transferable Bandwidth Allocation](https://mlsanity.com/paper/2401.10253/) (2023)

## Abstract (arXiv)

This paper designs a graph neural network (GNN) to improve bandwidth allocations for multiple legitimate wireless users transmitting to a base station in the presence of an eavesdropper. To improve the privacy and prevent eavesdropping attacks, we propose a user scheduling algorithm to schedule users satisfying an instantaneous minimum secrecy rate constraint. Based on this, we optimize the bandwidth allocations with three algorithms namely iterative search (IvS), GNN-based supervised learning (GNN-SL), and GNN-based unsupervised learning (GNN-USL). We present a computational complexity analysis which shows that GNN-SL and GNN-USL can be more efficient compared to IvS which is limited by the bandwidth block size. Numerical simulation results highlight that our proposed GNN-based resource allocations can achieve a comparable sum secrecy rate compared to IvS with significantly lower computational complexity. Furthermore, we observe that the GNN approach is more robust to uncertainties in the eavesdropper's channel state information, especially compared with the best channel allocation scheme.