Our manuscript entitled “Link Scheduling Using Graph Neural Networks” is submitted to IEEE Journal on Selected Topics in Signal Processing.

In this work arXiv, we address the link scheduling problem in wireless multi-hop networks with orthogonal access, by incorporating machine learning over graphs into conventional algorithmic frameworks. Our work proposes both centralized and distributed solutions, which can improve the bandwidth efficiency of wireless multi-hop networks at low computational and communication costs.

This manuscript extends our conference paper, “Distributed Scheduling Using Graph Neural Networks” published in IEEE ICASSP 2021, from multiple aspects, including more detailed description, new methods of centralized scheduling, structural variations of the distributed solution for better performance or complexity, as well as additional numerical results.