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.

设计了一种图神经网络 (GNN) 来提高多个合法无线用户向基站传输数据时的带宽分配，在存在窃听者的情况下提高隐私性并防止窃听攻击，通过用户调度算法来满足瞬时最小保密率约束，并基于此优化了带宽分配，通过迭代搜索 (IvS)、基于 GNN 的监督学习 (GNN-SL) 和基于 GNN 的无监督学习 (GNN-USL) 三种算法。计算复杂度分析表明 GNN-SL 和 GNN-USL 相对于受带宽块大小限制的 IvS 来说更高效。数值模拟结果表明，我们提出的基于 GNN 的资源分配与 IvS 相比可以实现可比较的总保密率，同时计算复杂度显著降低。此外，我们观察到 GNN 方法在窃听者信道状态信息不确定性方面更为稳健，特别是与最佳信道分配方案相比。