Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributed Link Sparsification for Scalable Scheduling Using Graph
  Neural Networks   Distributed scheduling algorithms for throughput or utility maximization in
dense wireless multi-hop networks can have overwhelmingly high overhead,
causing increased congestion, energy consumption, radio footprint, and security
vulnerability. For wireless networks with dense connectivity, we propose a
distributed scheme for link sparsification with graph convolutional networks
(GCNs), which can reduce the scheduling overhead while keeping most of the
network capacity. In a nutshell, a trainable GCN module generates node
embeddings as topology-aware and reusable parameters for a local decision
mechanism, based on which a link can withdraw itself from the scheduling
contention if it is not likely to win. In medium-sized wireless networks, our
proposed sparse scheduler beats classical threshold-based sparsification
policies by retaining almost $70\%$ of the total capacity achieved by a
distributed greedy max-weight scheduler with $0.4\%$ of the point-to-point
message complexity and $2.6\%$ of the average number of interfering neighbors
per link.

25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci