Accelerating Graph Neural Networks via Edge Pruning for Power Allocation
  in Wireless Networks
Lili Chen Jingge Zhu Jamie Evans
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.