Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Decentralized Wireless Resource Allocations with Graph Neural
  Networks   We consider the broad class of decentralized optimal resource allocation
problems in wireless networks, which can be formulated as a constrained
statistical learning problems with a localized information structure. We
develop the use of Aggregation Graph Neural Networks (Agg-GNNs), which process
a sequence of delayed and potentially asynchronous graph aggregated state
information obtained locally at each transmitter from multi-hop neighbors. We
further utilize model-free primal-dual learning methods to optimize performance
subject to constraints in the presence of delay and asynchrony inherent to
decentralized networks. We demonstrate a permutation equivariance property of
the resulting resource allocation policy that can be shown to facilitate
transference to dynamic network configurations. The proposed framework is
validated with numerical simulations that exhibit superior performance to
baseline strategies.

29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
1
The categories of this paper are: cs
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol