Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Graph Neural Network Aided Expectation Propagation Detector for MU-MIMO
  Systems   Multiuser massive multiple-input multiple-output (MU-MIMO) systems can be
used to meet high throughput requirements of 5G and beyond networks. In an
uplink MUMIMO system, a base station is serving a large number of users,
leading to a strong multi-user interference (MUI). Designing a high performance
detector in the presence of a strong MUI is a challenging problem. This work
proposes a novel detector based on the concepts of expectation propagation (EP)
and graph neural network, referred to as the GEPNet detector, addressing the
limitation of the independent Gaussian approximation in EP. The simulation
results show that the proposed GEPNet detector significantly outperforms the
state-of-the-art MU-MIMO detectors in strong MUI scenarios with equal number of
transmit and receive antennas.

5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
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The categories of this paper are: stat
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg