Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep learning for location based beamforming with NLOS channels   Massive MIMO systems are highly efficient but critically rely on accurate
channel state information (CSI) at the base station in order to determine
appropriate precoders. CSI acquisition requires sending pilot symbols which
induce an important overhead. In this paper, a method whose objective is to
determine an appropriate precoder from the knowledge of the user's location
only is proposed. Such a way to determine precoders is known as location based
beamforming. It allows to reduce or even eliminate the need for pilot symbols,
depending on how the location is obtained. the proposed method learns a direct
mapping from location to precoder in a supervised way. It involves a neural
network with a specific structure based on random Fourier features allowing to
learn functions containing high spatial frequencies. It is assessed empirically
and yields promising results on realistic synthetic channels. As opposed to
previously proposed methods, it allows to handle both line-of-sight (LOS) and
non-line-of-sight (NLOS) channels.

18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas