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

Halim Lee, Jiwon Seo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publication2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728161648
DOIs
Publication statusPublished - 2020 Nov 1
Event2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020 - Seoul, Korea, Republic of
Duration: 2020 Nov 12020 Nov 3

Publication series

Name2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020

Conference

Conference2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020
CountryKorea, Republic of
CitySeoul
Period20/11/120/11/3

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

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