Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace

Research Article

Deep Learning based OTDOA Positioning for NB-IoT Communication Systems

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  • @INPROCEEDINGS{10.4108/eai.27-8-2020.2294221,
        author={Guangjin  Pan and Tao  Wang and Xiufeng  Jiang and Shunqing  Zhang},
        title={Deep Learning based OTDOA Positioning for NB-IoT Communication Systems},
        proceedings={Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace},
        publisher={EAI},
        proceedings_a={MOBIMEDIA},
        year={2020},
        month={11},
        keywords={positioning nb-iot observed time difference of arrival deep neural network},
        doi={10.4108/eai.27-8-2020.2294221}
    }
    
  • Guangjin Pan
    Tao Wang
    Xiufeng Jiang
    Shunqing Zhang
    Year: 2020
    Deep Learning based OTDOA Positioning for NB-IoT Communication Systems
    MOBIMEDIA
    EAI
    DOI: 10.4108/eai.27-8-2020.2294221
Guangjin Pan1, Tao Wang1, Xiufeng Jiang1, Shunqing Zhang1,*
  • 1: School of Communication and Information Engineering, Shanghai University
*Contact email: shunqing@shu.edu.cn

Abstract

Positioning is becoming a key component in many Internet of Things (IoT) applications. The main challenges and limitations are the narrow bandwidth, low power and low cost which reduces the accuracy of the time of arrival (TOA) estimation. In this paper, we consider the positioning scenario of Narrowband IoT (NB-IoT) that can benefit from observed time difference of arrival (OTDOA). By applying the deep learning based technique, we explore the generalization and feature extraction abilities of neural networks to tackle the aforementioned challenges. As demonstrated in the numerical experiments, the proposed algorithm can be used in different inter-site distance situations and results in a 15% and 50% positioning accuracy improvement compared with Gauss-Newton method in LOS scenario and NLOS scenario respectively.