Urban Navigation with LTE using a Large Antenna Array and Machine Learning
(2022) IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)- Abstract
- Channel fingerprinting entails associating a point in space with measured properties of a received wireless signal. If the propagation environment for that point in space remains reasonably static with time, then a receiver with no knowledge of its own position experiencing a similar channel in the future might reasonably infer proximity to the original surveyed point. In this article, measurements of downlink LTE Common Reference Symbols from one sector of an eNodeB are used to generate channel fingerprints for a passenger vehicle driving through a dense urban environment without line-of-sight to the transmitter. Channel estimates in the global azimuthal-delay domain are used to create a navigation solution with meter-level accuracy... (More)
- Channel fingerprinting entails associating a point in space with measured properties of a received wireless signal. If the propagation environment for that point in space remains reasonably static with time, then a receiver with no knowledge of its own position experiencing a similar channel in the future might reasonably infer proximity to the original surveyed point. In this article, measurements of downlink LTE Common Reference Symbols from one sector of an eNodeB are used to generate channel fingerprints for a passenger vehicle driving through a dense urban environment without line-of-sight to the transmitter. Channel estimates in the global azimuthal-delay domain are used to create a navigation solution with meter-level accuracy around a city block. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/66536d2c-e50b-4603-9283-f7a06b4533f9
- author
- Whiton, Russ LU ; Chen, Junshi LU ; Johansson, Tobias and Tufvesson, Fredrik LU
- organization
- publishing date
- 2022-06-22
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)
- pages
- 5 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)
- conference location
- Helsinki, Finland
- conference dates
- 2022-06-19 - 2022-06-22
- external identifiers
-
- scopus:85137782912
- ISBN
- 978-1-6654-8243-1
- DOI
- 10.1109/VTC2022-Spring54318.2022.9860844
- language
- English
- LU publication?
- yes
- id
- 66536d2c-e50b-4603-9283-f7a06b4533f9
- date added to LUP
- 2022-10-04 15:55:15
- date last changed
- 2023-11-19 08:18:42
@inproceedings{66536d2c-e50b-4603-9283-f7a06b4533f9, abstract = {{Channel fingerprinting entails associating a point in space with measured properties of a received wireless signal. If the propagation environment for that point in space remains reasonably static with time, then a receiver with no knowledge of its own position experiencing a similar channel in the future might reasonably infer proximity to the original surveyed point. In this article, measurements of downlink LTE Common Reference Symbols from one sector of an eNodeB are used to generate channel fingerprints for a passenger vehicle driving through a dense urban environment without line-of-sight to the transmitter. Channel estimates in the global azimuthal-delay domain are used to create a navigation solution with meter-level accuracy around a city block.}}, author = {{Whiton, Russ and Chen, Junshi and Johansson, Tobias and Tufvesson, Fredrik}}, booktitle = {{2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)}}, isbn = {{978-1-6654-8243-1}}, language = {{eng}}, month = {{06}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Urban Navigation with LTE using a Large Antenna Array and Machine Learning}}, url = {{https://lup.lub.lu.se/search/files/125009895/VTS2022_machine_larnin_paper_2022_04_13_Final_Submission.pdf}}, doi = {{10.1109/VTC2022-Spring54318.2022.9860844}}, year = {{2022}}, }