Poster abstract Deep neural network-based telco outdoor localization

Yige Zhang, Weixiong Rao, Yu Xiao

Research output: Contribution to conferencePosterScientificpeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Number of pages2
Publication statusPublished - 4 Nov 2018
MoE publication typeNot Eligible
EventACM Conference on Embedded Networked Sensor Systems - Shenzhen, China
Duration: 4 Nov 20187 Nov 2018
Conference number: 16


ConferenceACM Conference on Embedded Networked Sensor Systems
Abbreviated titleSenSys
Internet address

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    Zhang, Y., Rao, W., & Xiao, Y. (2018). Poster abstract Deep neural network-based telco outdoor localization. 307-308. Poster session presented at ACM Conference on Embedded Networked Sensor Systems, Shenzhen, China.