Deep Neural Network-based Telco Outdoor Localization

Yige Zhang, Weixiong Rao, Yu Xiao

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaAbstractScientificvertaisarvioitu

Abstrakti

When Telecommunication (Telco) networks provide phone call and data services for mobile users, measurement record (MR) data is generated by mobile devices during each call/session. MR data reports the connection states, e.g., signal strength, between mobile devices and nearby base stations. Given the MR data, the literature has proposed various Telco localization approaches, to localize mobile devices. Unfortunately, such approaches typically estimate the individual position independently, and could compromise the temporal and spatial locality in underlying mobility patterns. To address this issue, in this paper, we propose a deep neural network-based localization approach, namely RecuLSTM, to automatically extract contextual features and predict the positions of mobile devices from an input sequence of MR data. Our preliminary experiment validates that RecuLSTM greatly outperforms three recent works [1, 2, 4] which suffer from 3.2×, 1.91× and 3.56× median errors on the dataset in a 2G GSM suburban area, respectively.
AlkuperäiskieliEnglanti
Sivut307-308
TilaJulkaistu - marraskuuta 2018
OKM-julkaisutyyppiEi oikeutettu
TapahtumaACM Conference on Embedded Networked Sensor Systems - Shenzhen, Kiina
Kesto: 4 marraskuuta 20187 marraskuuta 2018
Konferenssinumero: 16
http://sensys.acm.org/2018/

Conference

ConferenceACM Conference on Embedded Networked Sensor Systems
LyhennettäSenSys
MaaKiina
KaupunkiShenzhen
Ajanjakso04/11/201807/11/2018
www-osoite

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    Weixiong Rao

    Yu Xiao (Host)
    20 heinäkuuta 201824 heinäkuuta 2018

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    Siteeraa tätä

    Zhang, Y., Rao, W., & Xiao, Y. (2018). Deep Neural Network-based Telco Outdoor Localization. 307-308. Abstraktin lähde: ACM Conference on Embedded Networked Sensor Systems, Shenzhen, Kiina.