IEEE Transactions on Mobile Computing (IEEE TMC) 

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Transfer Learning-Based Outdoor Position Recovery with Telco Data

Abstract:

Telecommunication (Telco) outdoor position recovery aims to 
localize outdoor mobile devices by leveraging measurement 
report (MR) data. Unfortunately, Telco position recovery 
requires sufficient amount of MR samples across different 
areas and suffers from high data collection cost. For an 
area with scarce MR samples, it is hard to achieve good 
accuracy. In this paper, by leveraging the recently 
developed transfer learning techniques, we design a novel 
Telco position recovery framework, called TLoc, to transfer 
good models in the carefully selected source domains (those 
fine-grained small subareas) to a target one which 
originally suffers from poor localization accuracy. 
Specifically, TLoc introduces three dedicated components: 
1) a new coordinate space to divide an area of interest 
into smaller domains, 2) a similarity measurement to select 
best source domains, and 3) an adaptation of an existing 
transfer learning approach. To the best of our knowledge, 
TLoc is the first framework that demonstrates the efficacy 
of applying transfer learning in the Telco outdoor position
recovery. To exemplify, on the 2G GSM and 4G LTE MR 
datasets in Shanghai, TLoc outperforms a non-transfer 
approach by 27.58% and 26.12% less median errors, and 
further leads to 47.77% and 49.22% less median errors than 
a recent fingerprinting approach NBL.


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BibTeX:
@article{Zhang:TMC2020,
title = "Transfer Learning-Based Outdoor Position Recovery with Telco Data",
journal = "IEEE Transactions on Mobile Computing",
year = "2020",
author = "Yige Zhang, Aaron Yi Ding, Joerg Ott, Mingxuan Yuan, Jia Zeng, Kun Zhang, Weixiong Rao",
}
How to cite:

Yige Zhang, Aaron Yi Ding, Joerg Ott, Mingxuan Yuan, Jia Zeng, Kun Zhang, Weixiong Rao, "Transfer Learning-Based Outdoor Position Recovery with Telco Data", in IEEE Transactions on Mobile Computing, 2020. doi: 10.1109/TMC.2020.2968899.