Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stable and Transferable Wireless Resource Allocation Policies via
  Manifold Neural Networks   We consider the problem of resource allocation in large scale wireless
networks. When contextualizing wireless network structures as graphs, we can
model the limits of very large wireless systems as manifolds. To solve the
problem in the machine learning framework, we propose the use of Manifold
Neural Networks (MNNs) as a policy parametrization. In this work, we prove the
stability of MNN resource allocation policies under the absolute perturbations
to the Laplace-Beltrami operator of the manifold, representing system noise and
dynamics present in wireless systems. These results establish the use of MNNs
in achieving stable and transferable allocation policies for large scale
wireless networks. We verify our results in numerical simulations that show
superior performance relative to baseline methods.

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The categories of this paper are: hep-th
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The categories of this paper are: funct-an
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The categories of this paper are: math-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: physics
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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: stat
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The categories of this paper are: nucl-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-bio
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The categories of this paper are: ao-sci
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The categories of this paper are: comp-gas
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The categories of this paper are: cond-mat
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph