Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Preliminary Study of Machine-Learning-Based Ranging with LTE Channel
  Impulse Response in Multipath Environment   Alternative navigation technology to global navigation satellite systems
(GNSSs) is required for unmanned ground vehicles (UGVs) in multipath
environments (such as urban areas). In urban areas, long-term evolution (LTE)
signals can be received ubiquitously at high power without any additional
infrastructure. We present a machine learning approach to estimate the range
between the LTE base station and UGV based on the LTE channel impulse response
(CIR). The CIR, which includes information of signal attenuation from the
channel, was extracted from the LTE physical layer using a software-defined
radio (SDR). We designed a convolutional neural network (CNN) that estimates
ranges with the CIR as input. The proposed method demonstrated better ranging
performance than a received signal strength indicator (RSSI)-based method
during our field test.

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The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
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The categories of this paper are: hep-lat
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The categories of this paper are: gr-qc
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The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
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The categories of this paper are: alg-geom
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The categories of this paper are: atom-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
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The categories of this paper are: math
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The categories of this paper are: dg-ga
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The categories of this paper are: math-ph
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The categories of this paper are: funct-an
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The categories of this paper are: solv-int