Accelerating Graph Neural Networks via Edge Pruning for Power Allocation in Wireless Networks
This work addresses efficiency issues for wireless network optimization, but it is incremental as it builds on existing distance-based threshold methods.
The paper tackles the problem of high computational complexity in Graph Neural Networks (GNNs) for power allocation in wireless networks by introducing a neighbor-based threshold approach, reducing time complexity from O(|V|^2) to O(|V|) while maintaining strong performance and generalization.
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.