通过边剪枝加速图神经网络以用于无线网络中的功率分配
Accelerating Graph Neural Networks via Edge Pruning for Power Allocation in Wireless Networks
AI总结：
本文首次提出基于邻居的阈值方法对图神经网络进行边剪枝，用于解决无线网络中的功率分配问题，在保持性能和泛化能力的同时，将时间复杂度从O(|V|^2)降至O(|V|)。
摘要：
图神经网络（GNNs）近期已成为解决无线网络中功率分配问题的一种极具前景的方法。由于未配对的发射机和接收机通常在空间上相距较远，因此提出了基于距离的阈值方法，通过在GNNs中排除或包含信道状态信息来减少计算时间。在本文中，我们首次将基于邻居的阈值方法引入GNNs以降低时间复杂度。此外，我们对基于距离和基于邻居的阈值进行了全面分析，并提供了在不同通信信道场景下选择合适值的建议。我们设计了相应的基于邻居的图神经网络（N-GNN），旨在分配发射功率以最大化网络吞吐量。我们的结果表明，我们提出的N-GNN在降低时间复杂度方面具有显著优势，同时保持了强大的性能和泛化能力。此外，我们表明通过选择合适的阈值，时间复杂度从O(|V|^2)降低到O(|V|)，其中|V|是收发器对的总数。
英文摘要：
Graph 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 distance-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 neighbour-based Graph Neural Networks (N-GNN) with the aim of allocating transmit powers to maximise the network throughput. Our results show that our proposed N-GNN offer significant advantages in terms of reducing time complexity while preserving strong performance and generalisation capacity. 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.
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