Neural-network-assisted UE Localization Using Radio-channel Fingerprints in LTE Networks
â??In this paper, a novel fingerprint-based localization
technique is proposed which is applicable for positioning User
Equipments (UEs) in cellular communication networks such as
the Long-Term-Evolution (LTE) system. This technique utilizes
a unique mapping between the characteristics of a radio channel
formulated as a fingerprint vector and a geographical location.
A feature-extraction algorithm is applied to selecting channel
parameters with non-redundant information that are calculated
from the LTE down-link signals. A feed-forward neural network
with the input of fingerprint vectors and the output of UEsâ??
known locations is trained and used by UEs to estimate their
positions. The results of experiments conducted in an in-service
LTE system demonstrate that by using only one LTE eNodeB,
the proposed technique yields a median error distance of 6 and
75 meters in indoor and outdoor environments respectively. This
localization technique is applicable in the cases where the Global
Navigation Satellite System (GNSS) is unavailable, e.g. in indoor
environments or in dense-urban scenarios with closely-spaced
skyscrapers heavily blocking the line-of-sight paths between a
UE and GNSS satellites.
KeyWords
Long-Term-Evolution, fingerprint, radio propagation,
channel impulse response, multipath component, feature
extraction, neural networks, and user equipment localization
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