We consider the problem of binary power control, or link scheduling, in wireless interference networks, where the power control policy is trained using graph representation learning. We leverage the interference graph of the wireless network as an underlying topology for a graph neural network (GNN) backbone, which converts the channel matrix to a set of node embeddings for all transmitter-receiver pairs. We show how the node embeddings can be trained in several ways, including via supervised, unsupervised, and self-supervised learning, and we compare the impact of different supervision levels on the performance of these methods in terms of the system-level throughput, convergence behavior, sample efficiency, and generalization capability.

翻译：我们考虑无线干扰网络的二元电源控制或连接调度问题,在无线干扰网络中,电源控制政策是使用图形演示学习来培训的。我们利用无线网络的干扰图作为图形神经网络主干网的基本地形学,该主干网将频道矩阵转换成一套供所有接收发射机的对子使用的节点嵌入器。我们展示了节点嵌入如何以多种方式培训,包括通过监督、不受监督和自我监督的学习,我们比较了不同级别的监督水平在系统水平吞吐、趋同行为、抽样效率和一般化能力方面对这些方法的性能的影响。