Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning Assisted mmWave Beam Prediction with Prior Low-frequency
  Information   Huge overhead of beam training poses a significant challenge to mmWave
communications. To address this issue, beam tracking has been widely
investigated whereas existing methods are hard to handle serious multipath
interference and non-stationary scenarios. Inspired by the spatial similarity
between low-frequency and mmWave channels in non-standalone architectures, this
paper proposes to utilize prior low-frequency information to predict the
optimal mmWave beam, where deep learning is adopted to enhance the prediction
accuracy. Specifically, periodically estimated low-frequency channel state
information (CSI) is applied to track the movement of user equipment, and
timing offset indicator is proposed to indicate the instant of mmWave beam
training relative to low-frequency CSI estimation. Meanwhile, long-short term
memory networks based dedicated models are designed to implement the
prediction. Simulation results show that our proposed scheme can achieve higher
beamforming gain than the conventional methods while requiring little overhead
of mmWave beam training.

6
The categories of this paper are: astro-ph
1
The categories of this paper are: cs
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg