Due to mutual interference between users, power allocation problems in wireless networks are often non-convex and computationally challenging. Graph neural networks (GNNs) have recently emerged as a promising approach to tackling these problems and an approach that exploits the underlying topology of wireless networks. In this paper, we propose a novel graph representation method for wireless networks that include full-duplex (FD) nodes. We then design a corresponding FD Graph Neural Network (F-GNN) with the aim of allocating transmit powers to maximise the network throughput. Our results show that our F-GNN achieves state-of-art performance with significantly less computation time. Besides, F-GNN offers an excellent trade-off between performance and complexity compared to classical approaches. We further refine this trade-off by introducing a distance-based threshold for inclusion or exclusion of edges in the network. We show that an appropriately chosen threshold reduces required training time by roughly 20% with a relatively minor loss in performance.

翻译：由于用户间存在相互干扰，无线网络中的功率分配问题通常是非凸且计算复杂度较高的。图神经网络（GNN）近期已成为解决这类问题并利用无线网络底层拓扑结构的一种有前景的方法。本文针对包含全双工（FD）节点的无线网络，提出了一种新型图表示方法。随后，我们设计了相应的全双工图神经网络（F-GNN），旨在分配发射功率以最大化网络吞吐量。结果表明，我们的F-GNN在显著降低计算时间的同时实现了最先进的性能。此外，与经典方法相比，F-GNN在性能与复杂度之间提供了出色的权衡。我们通过引入基于距离的边包含/排除阈值，进一步优化了这一权衡。研究表明，适当选择的阈值可将训练时间减少约20%，且性能损失相对较小。