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RSRP-Based CID Outdoor Positioning via LTE Networks

Authors:

Huan ZhangAuthors Info & Claims

ICBDT '20: Proceedings of the 3rd International Conference on Big Data Technologies

Pages 116 - 120

https://doi.org/10.1145/3422713.3424569

Published: 23 October 2020 Publication History

Abstract

Precise positioning of outdoor user equipment (UE) is a hot spot for mobile communication. Satellite navigation and positioning technology has low positioning accuracy in complicated scenarios. In this paper, we propose a Cell-ID (CID) based positioning method via Long-Time-Evolution (LTE) network using reference signal received power (RSRP). By using the generalized regression neural network (GRNN) algorithm, precise positioning of UE is realized with a small amount of data. The performance of the proposed method is acquired by the means of cumulative distribution function (CDF). The experiment compares the effects of data preprocessing, CID information and training methods on positioning accuracy. And the experimental results prove the feasibility and effectiveness of the positioning method in complicated environments, with the positioning accuracy of 12.7m.

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Cited By

Le Floch AKacimi RDruart PLefebvre YBeylot ABoukerche ADe Rango FSun Z(2023)Accurate E-CID Framework for Indoor Positioning in 5G using Path Tracing and Machine LearningProceedings of the Int'l ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems10.1145/3616388.3623406(9-17)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1145/3616388.3623406

Index Terms

RSRP-Based CID Outdoor Positioning via LTE Networks
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms

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ICBDT '20: Proceedings of the 3rd International Conference on Big Data Technologies

September 2020

250 pages

ISBN:9781450387859

DOI:10.1145/3422713

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 23 October 2020

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ICBDT 2020

ICBDT 2020: 2020 3rd International Conference on Big Data Technologies

September 18 - 20, 2020

Qingdao, China

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Cited By

Le Floch AKacimi RDruart PLefebvre YBeylot ABoukerche ADe Rango FSun Z(2023)Accurate E-CID Framework for Indoor Positioning in 5G using Path Tracing and Machine LearningProceedings of the Int'l ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems10.1145/3616388.3623406(9-17)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1145/3616388.3623406

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