A Preliminary Study of Machine-Learning-Based Ranging with LTE Channel Impulse Response in Multipath Environment

Halim Lee; Jiwon Seo

doi:10.1109/ICCE-Asia49877.2020.9277218

A Preliminary Study of Machine-Learning-Based Ranging with LTE Channel Impulse Response in Multipath Environment

Halim Lee, Jiwon Seo

School of Integrated Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 Citations (Scopus)

Abstract

Alternative navigation technology to global navigation satellite systems (GNSSs) is required for unmanned ground vehicles (UGVs) in multipath environments (such as urban areas). In urban areas, long-term evolution (LTE) signals can be received ubiquitously at high power without any additional infrastructure. We present a machine learning approach to estimate the range between the LTE base station and UGV based on the LTE channel impulse response (CIR). The CIR, which includes information of signal attenuation from the channel, was extracted from the LTE physical layer using a software-defined radio (SDR). We designed a convolutional neural network (CNN) that estimates ranges with the CIR as input. The proposed method demonstrated better ranging performance than a received signal strength indicator (RSSI)-based method during our field test.

Original language	English
Title of host publication	2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728161648
DOIs	https://doi.org/10.1109/ICCE-Asia49877.2020.9277218
Publication status	Published - 2020 Nov 1
Event	2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020 - Seoul, Korea, Republic of Duration: 2020 Nov 1 → 2020 Nov 3

Publication series

Name	2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020

Conference

Conference	2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020
Country/Territory	Korea, Republic of
City	Seoul
Period	20/11/1 → 20/11/3

Bibliographical note

Funding Information:
This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the High- Potential Individuals Global Training Program (2020- 0-01531) supervised by the Institute for Information & Communications Technology Planning & Evaluation (IITP).

Publisher Copyright:
© 2020 IEEE.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

All Science Journal Classification (ASJC) codes

Computer Science Applications
Electrical and Electronic Engineering
Media Technology
Instrumentation

Access to Document

10.1109/ICCE-Asia49877.2020.9277218

Cite this

Lee, H., & Seo, J. (2020). A Preliminary Study of Machine-Learning-Based Ranging with LTE Channel Impulse Response in Multipath Environment. In 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020 Article 9277218 (2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-Asia49877.2020.9277218

Lee, Halim ; Seo, Jiwon. / A Preliminary Study of Machine-Learning-Based Ranging with LTE Channel Impulse Response in Multipath Environment. 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020).

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abstract = "Alternative navigation technology to global navigation satellite systems (GNSSs) is required for unmanned ground vehicles (UGVs) in multipath environments (such as urban areas). In urban areas, long-term evolution (LTE) signals can be received ubiquitously at high power without any additional infrastructure. We present a machine learning approach to estimate the range between the LTE base station and UGV based on the LTE channel impulse response (CIR). The CIR, which includes information of signal attenuation from the channel, was extracted from the LTE physical layer using a software-defined radio (SDR). We designed a convolutional neural network (CNN) that estimates ranges with the CIR as input. The proposed method demonstrated better ranging performance than a received signal strength indicator (RSSI)-based method during our field test.",

author = "Halim Lee and Jiwon Seo",

note = "Funding Information: This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the High- Potential Individuals Global Training Program (2020- 0-01531) supervised by the Institute for Information & Communications Technology Planning & Evaluation (IITP). Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020 ; Conference date: 01-11-2020 Through 03-11-2020",

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doi = "10.1109/ICCE-Asia49877.2020.9277218",

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Lee, H & Seo, J 2020, A Preliminary Study of Machine-Learning-Based Ranging with LTE Channel Impulse Response in Multipath Environment. in 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020., 9277218, 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020, Seoul, Korea, Republic of, 20/11/1. https://doi.org/10.1109/ICCE-Asia49877.2020.9277218

A Preliminary Study of Machine-Learning-Based Ranging with LTE Channel Impulse Response in Multipath Environment. / Lee, Halim; Seo, Jiwon.
2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9277218 (2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - Alternative navigation technology to global navigation satellite systems (GNSSs) is required for unmanned ground vehicles (UGVs) in multipath environments (such as urban areas). In urban areas, long-term evolution (LTE) signals can be received ubiquitously at high power without any additional infrastructure. We present a machine learning approach to estimate the range between the LTE base station and UGV based on the LTE channel impulse response (CIR). The CIR, which includes information of signal attenuation from the channel, was extracted from the LTE physical layer using a software-defined radio (SDR). We designed a convolutional neural network (CNN) that estimates ranges with the CIR as input. The proposed method demonstrated better ranging performance than a received signal strength indicator (RSSI)-based method during our field test.

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M3 - Conference contribution

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Lee H, Seo J. A Preliminary Study of Machine-Learning-Based Ranging with LTE Channel Impulse Response in Multipath Environment. In 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9277218. (2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020). doi: 10.1109/ICCE-Asia49877.2020.9277218

A Preliminary Study of Machine-Learning-Based Ranging with LTE Channel Impulse Response in Multipath Environment

Abstract

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Bibliographical note

All Science Journal Classification (ASJC) codes

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