Deep Learning based OTDOA Positioning for NB-IoT Communication Systems

Pan, Guangjin and Wang, Tao and Jiang, Xiufeng and Zhang, Shunqing (2020) Deep Learning based OTDOA Positioning for NB-IoT Communication Systems. In: Mobimedia 2020, 27-28 August 2020, Cyberspace.

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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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: positioning nb-iot observed time difference of arrival deep neural network
Subjects: Q Science > QA Mathematics > QA76 Computer software
Depositing User: EAI Editor I.
Date Deposited: 04 Feb 2021 14:19
Last Modified: 04 Feb 2021 14:19
URI: https://eprints.eudl.eu/id/eprint/846

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