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Neural-Network-Assisted UE Localization Using Radio-Channel Fingerprints in LTE Networks

Ye, Xiaokang ; Yin, Xuefeng ; Cai, Xuesong LU ; Perez Yuste, Antonio and Xu, Hongliang (2017) In IEEE Access 5. p.12071-12087
Abstract

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 feedforward neural network with the input of fingerprint vectors and the output of UEs' known locations is trained and used by UEs to estimate their positions. The results of experiments conducted in an in-service LTE... (More)

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 feedforward neural network with the input of fingerprint vectors and the output of UEs' 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.

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author
; ; ; and
publishing date
type
Contribution to journal
publication status
published
subject
keywords
channel impulse response, feature extraction, fingerprint, Long-term-evolution, multipath component, neural networks, radio propagation, user equipment localization
in
IEEE Access
volume
5
article number
7938617
pages
17 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85029033695
ISSN
2169-3536
DOI
10.1109/ACCESS.2017.2712131
language
English
LU publication?
no
additional info
Funding Information: This work was supported in part by the key project 5G Ka Frequency Bands and Higher and Lower Frequency Band Cooperative Trail System Research and Development of China Ministry of Industry and Information Technology under Grant 2016ZX03001015, in part by the Key Program of National Natural Science Foundation of China (NSFC) under Grant 61331009, in part by the general project of NSFC under Grant 61471268, in part by the HongKong, Macao, and Taiwan Science and Technology Cooperation Program of China under Grant 2014DFT10290, and in part by the project Development of a Performance Evaluation System for Localization Techniques/Equipments Utilizing Time-Difference-of-Arrival Information of Shanghai Radio Monitoring Station. Publisher Copyright: © 2013 IEEE.
id
324bc62e-066a-4869-aafa-5f0c5ba53cd4
date added to LUP
2021-11-22 22:26:56
date last changed
2022-05-24 17:57:08
@article{324bc62e-066a-4869-aafa-5f0c5ba53cd4,
  abstract     = {{<p>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 feedforward neural network with the input of fingerprint vectors and the output of UEs' 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.</p>}},
  author       = {{Ye, Xiaokang and Yin, Xuefeng and Cai, Xuesong and Perez Yuste, Antonio and Xu, Hongliang}},
  issn         = {{2169-3536}},
  keywords     = {{channel impulse response; feature extraction; fingerprint; Long-term-evolution; multipath component; neural networks; radio propagation; user equipment localization}},
  language     = {{eng}},
  pages        = {{12071--12087}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Access}},
  title        = {{Neural-Network-Assisted UE Localization Using Radio-Channel Fingerprints in LTE Networks}},
  url          = {{http://dx.doi.org/10.1109/ACCESS.2017.2712131}},
  doi          = {{10.1109/ACCESS.2017.2712131}},
  volume       = {{5}},
  year         = {{2017}},
}