Graph Neural Network-Based Bandwidth Allocation for Secure Wireless
  Communications
Xin Hao Phee Lep Yeoh Yuhong Liu Changyang She Branka Vucetic Yonghui Li
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.