Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning based Joint Precoder Design and Antenna Selection for
  Partially Connected Hybrid Massive MIMO Systems   Efficient resource allocation with hybrid precoder design is essential for
massive MIMO systems operating in millimeter wave (mmW) domain. Owing to a
higher energy efficiency and a lower complexity of a partially connected hybrid
architecture, in this letter, we propose a joint deep convolutional neural
network (CNN) based scheme for precoder design and antenna selection of a
partially connected massive MIMO hybrid system. Precoder design and antenna
selection is formulated as a regression and classification problem,
respectively, for CNN. The channel data is fed to the first CNN network which
outputs a subset of selected antennas having the optimal spectral efficiency.
This subset is again fed to the second CNN to obtain the block diagonal
precoder for a partially connected architecture. Simulation results verifies
the superiority of CNN based approach over conventional iterative and
alternating minimization (alt-min) algorithms. Moreover, the proposed scheme is
computationally efficient and is not very sensitive to channel irregularities.

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The categories of this paper are: hep-lat
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The categories of this paper are: dg-ga
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The categories of this paper are: econ
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The categories of this paper are: stat
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The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
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The categories of this paper are: q-alg
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: supr-con
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The categories of this paper are: cmp-lg
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The categories of this paper are: alg-geom