  In wireless multi-hop networks, delay is an important metric for many
applications. However, the max-weight scheduling algorithms in the literature
typically focus on instantaneous optimality, in which the schedule is selected
by solving a maximum weighted independent set (MWIS) problem on the
interference graph at each time slot. These myopic policies perform poorly in
delay-oriented scheduling, in which the dependency between the current backlogs
of the network and the schedule of the previous time slot needs to be
considered. To address this issue, we propose a delay-oriented distributed
scheduler based on graph convolutional networks (GCNs). In a nutshell, a
trainable GCN module generates node embeddings that capture the network
topology as well as multi-step lookahead backlogs, before calling a distributed
greedy MWIS solver. In small- to medium-sized wireless networks with
heterogeneous transmit power, where a few central links have many interfering
neighbors, our proposed distributed scheduler can outperform the myopic
schedulers based on greedy and instantaneously optimal MWIS solvers, with good
generalizability across graph models and minimal increase in communication
complexity.
