Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Real-time Outdoor Localization Using Radio Maps: A Deep Learning
  Approach   This paper deals with the problem of localization in a cellular network in a
dense urban scenario. Global Navigation Satellite Systems typically perform
poorly in urban environments, where the likelihood of line-of-sight conditions
between the devices and the satellites is low, and thus alternative
localization methods are required for good accuracy. We present LocUNet: A
fully convolutional, end-to-end trained neural network for the localization
task, which merely depends on the received signal strengths (RSS) from Base
Stations (BSs).In a wireless network, user devices scan the base station beacon
slots and identify the few strongest base station signals for handover and
user-base station association purposes. In the proposed method, the user to be
localized simply reports such received signal strengths to a central processing
unit, which may be located in the cloud. Alternatively, the localization can be
performed locally at the user. Using the pathloss radio map estimations and the
RSS measurements, LocUNet can localize users with state-of-the-art accuracy and
enjoys high robustness to inaccuracies in the estimations of the radio maps.
The proposed method does not require pre-sampling of the environment; and is
suitable for real-time applications, thanks to the RadioUNet, a neural
network-based radio map estimator. Moreover, two novel datasets that allow for
numerical evaluations of RSS and ToA methods in realistic urban environments
are presented and set publicly available for the use of research community. By
using these datasets, we also provided a fair comparison of state-of-the-art
RSS and ToA-based methods in the dense urban scenario, LocUNet outperforming
all the compared methods.

18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga