Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Novel Multi-Task Learning Empowered Codebook Design for Downlink SCMA
  Networks   Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal
multiple access (NOMA) scheme for the enabling of massive machine-type
communication. In SCMA, the design of good sparse codebooks and efficient
multiuser decoding have attracted tremendous research attention in the past few
years. This paper aims to leverage deep learning to jointly design the downlink
SCMA encoder and decoder with the aid of autoencoder. We introduce a novel
end-to-end learning based SCMA (E2E-SCMA) design framework, under which
improved sparse codebooks and low-complexity decoder are obtained. Compared to
conventional SCMA schemes, our numerical results show that the proposed
E2E-SCMA leads to significant improvements in terms of error rate and
computational complexity.

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The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ex
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The categories of this paper are: adap-org
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The categories of this paper are: cond-mat
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The categories of this paper are: q-alg
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
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The categories of this paper are: physics
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The categories of this paper are: hep-th
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The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg