Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Low-Rank Matrix Completion for Mobile Edge Caching in Fog-RAN via
  Riemannian Optimization   The upcoming big data era is likely to demand tremendous computation and
storage resources for communications. By pushing computation and storage to
network edges, fog radio access networks (Fog-RAN) can effectively increase
network throughput and reduce transmission latency. Furthermore, we can exploit
the benefits of cache enabled architecture in Fog-RAN to deliver contents with
low latency. Radio access units (RAUs) need content delivery from fog servers
through wireline links whereas multiple mobile devices acquire contents from
RAUs wirelessly. This work proposes a unified low-rank matrix completion (LRMC)
approach to solving the content delivery problem in both wireline and wireless
parts of Fog-RAN. To attain a low caching latency, we present a high precision
approach with Riemannian trust-region method to solve the challenging LRMC
problem by exploiting the quotient manifold geometry of fixed-rank matrices.
Numerical results show that the new approach has a faster convergence rate, is
able to achieve optimal results, and outperforms other state-of-art algorithms.

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The categories of this paper are: acc-phys
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The categories of this paper are: econ
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: cmp-lg
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The categories of this paper are: dg-ga
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The categories of this paper are: chem-ph
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The categories of this paper are: eess
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The categories of this paper are: gr-qc
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The categories of this paper are: stat
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The categories of this paper are: hep-lat
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The categories of this paper are: cond-mat
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The categories of this paper are: astro-ph