Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Low-Dimensionality of Noise-Free RSS and its Application in Distributed
  Massive MIMO   We examine the dimensionality of noise-free uplink received signal strength
(RSS) data in a distributed multiuser massive multiple-input multiple-output
system. Specifically, we apply principal component analysis to the noise-free
uplink RSS and observe that it has a low-dimensional principal subspace. We
make use of this unique property to propose RecGP - a reconstruction-based
Gaussian process regression (GP) method which predicts user locations from
uplink RSS data. Considering noise-free RSS for training and noisy test RSS for
location prediction, RecGP reconstructs the noisy test RSS from a low-
dimensional principal subspace of the noise-free training RSS. The
reconstructed RSS is input to a trained GP model for location prediction. Noise
reduction facilitated by the reconstruction step allows RecGP to achieve lower
prediction error than standard GP methods which directly use the test RSS for
location prediction.

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The categories of this paper are: astro-ph
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The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
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The categories of this paper are: supr-con
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The categories of this paper are: bayes-an
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The categories of this paper are: gr-qc
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The categories of this paper are: math-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: nlin
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The categories of this paper are: eess
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The categories of this paper are: q-fin
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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: chao-dyn