Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Network-Based Ranging with LTE Channel Impulse Response for
  Localization in Indoor Environments   A neural network (NN)-based approach for indoor localization via cellular
long-term evolution (LTE) signals is proposed. The approach estimates, from the
channel impulse response (CIR), the range between an LTE eNodeB and a receiver.
A software-defined radio (SDR) extracts the CIR, which is fed to a long
short-term memory model (LSTM) recurrent neural network (RNN) to estimate the
range. Experimental results are presented comparing the proposed approach
against a baseline RNN without LSTM. The results show a receiver navigating for
100 m in an indoor environment, while receiving signals from one LTE eNodeB.
The ranging root-mean squared error (RMSE) and ranging maximum error along the
receiver's trajectory were reduced from 13.11 m and 55.68 m, respectively, in
the baseline RNN to 9.02 m and 27.40 m, respectively, with the proposed
RNN-LSTM.

13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
1
The categories of this paper are: cs
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph