  A fundamental problem in the design of wireless networks is to efficiently
schedule transmission in a distributed manner. The main challenge stems from
the fact that optimal link scheduling involves solving a maximum weighted
independent set (MWIS) problem, which is NP-hard. For practical link scheduling
schemes, distributed greedy approaches are commonly used to approximate the
solution of the MWIS problem. However, these greedy schemes mostly ignore
important topological information of the wireless networks. To overcome this
limitation, we propose a distributed MWIS solver based on graph convolutional
networks (GCNs). In a nutshell, a trainable GCN module learns topology-aware
node embeddings that are combined with the network weights before calling a
greedy solver. In small- to middle-sized wireless networks with tens of links,
even a shallow GCN-based MWIS scheduler can leverage the topological
information of the graph to reduce in half the suboptimality gap of the
distributed greedy solver with good generalizability across graphs and minimal
increase in complexity.
