Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning-based Remote Radio Head Selection and Localization in
  Distributed Antenna System   In this work, we consider estimating user positions in a spatially
distributed antenna system (DAS) from the uplink channel state information
(CSI). However, with the increased number of remote radio heads (RRHs),
collecting CSI at a central unit (CU) can significantly increase the fronthaul
overhead and computational complexity of the CU. This problem can be mitigated
by selecting a subset of RRHs. Thus, we present a deep learning-based approach
to select a subset of RRHs for wireless localization. We employ an RRH
selection layer that is jointly trained with the rest of the network and learn
the model parameters as well as the set of selected RRHs. We show that the
selection strategy comes at a relatively small cost of localization
performance. Nonetheless, by comparison to a trivial approach based on the
maximization of the channel gain, we show that the proposed method leads to
significant performance gains in a propagation environment dominated by
non-line-of-sight.

36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex