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：由于用户间的相互干扰，无线网络中的功率分配问题往往非凸且计算难度大。近年来，图神经网络（GNN）作为一种利用无线网络底层拓扑结构解决此类问题的有效方法崭露头角。本文提出了一种针对包含全双工（FD）节点的无线网络的图表示新方法，并设计了相应的全双工图神经网络（F-GNN），旨在通过分配发射功率最大化网络吞吐量。实验结果表明，我们的F-GNN在显著降低计算时间的同时达到了最先进的性能。此外，与传统方法相比，F-GNN在性能与复杂度之间实现了优异权衡。我们进一步引入基于距离的阈值来控制网络中边的包含与否，从而优化这一权衡。研究发现，恰当选择的阈值可将训练时间减少约20%，且仅造成相对较小的性能损失。