Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Study on MIMO Channel Estimation by 2D and 3D Convolutional Neural
  Networks   In this paper, we study the usage of Convolutional Neural Network (CNN)
estimators for the task of Multiple-Input-Multiple-Output Orthogonal Frequency
Division Multiplexing (MIMO-OFDM) Channel Estimation (CE). Specifically, the
CNN estimators interpolate the channel values of reference signals for
estimating the channel of the full OFDM resource element (RE) matrix. We have
designed a 2D CNN architecture based on U-net, and a 3D CNN architecture for
handling spatial correlation. We investigate the performance of various CNN
architectures fora diverse data set generated according to the 5G NR standard
and in particular, we investigate the influence of spatial correlation,
Doppler, and reference signal resource allocation. The CE CNN estimators are
then integrated with MIMO detection algorithms for testing their influence on
the system level Bit Error Rate(BER) performance.

36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int