Neural Networks (GNNs) have recently emerged as a promising approach to
tackling power allocation problems in wireless networks. Since unpaired
transmitters and receivers are often spatially distant, the distanced-based
threshold is proposed to reduce the computation time by excluding or including
the channel state information in GNNs. In this paper, we are the first to
introduce a neighbour-based threshold approach to GNNs to reduce the time
complexity. Furthermore, we conduct a comprehensive analysis of both
distance-based and neighbour-based thresholds and provide recommendations for
selecting the appropriate value in different communication channel scenarios.
We design the corresponding distance-based and neighbour-based Graph Neural
Networks with the aim of allocating transmit powers to maximise the network
throughput. Our results show that our proposed GNNs offer significant
advantages in terms of reducing time complexity while preserving strong
performance. Besides, we show that by choosing a suitable threshold, the time
complexity is reduced from O(|V|^2) to O(|V|), where |V| is the total number of
transceiver pairs.

本研究提出了基于邻居节点的阈值方法，用于减少无线网络中图神经网络算法的复杂度，同时保持较强的性能，并分析了不同通信信道情况下适当选择距离阈值和邻居阈值的建议。结果表明，所提出的 GNN 方法在降低时间复杂度方面具有显着优势，并且通过选择适当的阈值，时间复杂度可以从 O (|V|^2) 降低到 O (|V|)。