DeepLoc: Deep Neural Network-based Telco Localization

Yige Zhang, Yu Xiao, Kai Zhao, Weixiong Rao

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Recent years have witnessed unprecedented amounts of telecommunication (Telco) data generated by Telco radio and core equipment. For example, measurement records (MRs) are generated to report the connection states, e.g., received signal strength at the mobile device, when mobile devices give phone calls or access data services. Telco historical data (e.g., MRs) have been widely analyzed to understand human mobility and optimize the applications such as urban planning and traffic forecasting. The key of these applications is to precisely localize outdoor mobile devices from these historical MR data. Previous works calculate the location of a mobile device based on each single MR sample, ignoring the sequential and temporal locality hidden in the consecutive MR samples. To address the issue, we propose a deep neural network (DNN)-based localization framework namely DeepLoc to ensemble a recently popular sequence learning model LSTM and a CNN. Without skillful feature design and post-processing steps, DeepLoc can generate a smooth trajectory consisting of accurately predicted locations. Extensive evaluation on 6 datasets collected at three representative areas (core business, urban and suburban areas in Shanghai, China) indicates that DeepLoc greatly outperforms 10 counterparts.
Original languageEnglish
Title of host publicationInternational Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
Number of pages10
ISBN (Electronic)9781450372831
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services - Houston, United States
Duration: 12 Nov 201914 Nov 2019
Conference number: 16


ConferenceInternational Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
Abbreviated titleMOBIQUITOUS
Country/TerritoryUnited States
Internet address


  • outdoor localization
  • deep learning
  • telecommunications


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  • Best Paper Award

    Xiao, Yu (Recipient), Nov 2019

    Prize: Award or honor granted for a specific work

  • Weixiong Rao

    Yu Xiao (Host)

    20 Jul 201824 Jul 2018

    Activity: Hosting a visitor typesHosting a visitor

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