High-Precision Machine-Learning Based Indoor Localization with Massive MIMO System
Tian, Guoda LU ; Yaman, Ilayda LU ; Sandra, Michiel LU ; Cai, Xuesong LU ; Liu, Liang LU
and Tufvesson, Fredrik
LU
(2023)
IEEE International Conference on Communications, ICC 2023
p.3690-3695
- Abstract
- High-precision cellular-based localization is one of the key technologies for next-generation communication systems. In this paper, we investigate the potential of applying machine learning (ML) to a massive multiple-input multiple-output (MIMO) system to enhance localization accuracy. We analyze a new ML-based localization pipeline that has two parallel fully connected neural networks (FCNN). The first FCNN takes the instantaneous spatial covariance matrix to capture angular information, while the second FCNN takes the channel impulse responses to capture delay information. We fuse the estimated coordinates of these two FCNNs for further accuracy improvement. To test the localization algorithm, we performed an indoor measurement campaign... (More)
- High-precision cellular-based localization is one of the key technologies for next-generation communication systems. In this paper, we investigate the potential of applying machine learning (ML) to a massive multiple-input multiple-output (MIMO) system to enhance localization accuracy. We analyze a new ML-based localization pipeline that has two parallel fully connected neural networks (FCNN). The first FCNN takes the instantaneous spatial covariance matrix to capture angular information, while the second FCNN takes the channel impulse responses to capture delay information. We fuse the estimated coordinates of these two FCNNs for further accuracy improvement. To test the localization algorithm, we performed an indoor measurement campaign with a massive MIMO testbed at 3.7 GHz. In the measured scenario, the proposed pipeline can achieve centimeter-level accuracy by combining delay and angular information. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/f13d0b4e-6ad9-40db-ad31-51aec0be1cf2
- author
- Tian, Guoda
LU
; Yaman, Ilayda
LU
; Sandra, Michiel
LU
; Cai, Xuesong
LU
; Liu, Liang
LU
and Tufvesson, Fredrik
LU
- organization
-
- Communications Engineering (research group)
- ELLIIT: the Linköping-Lund initiative on IT and mobile communication
- LTH Profile Area: AI and Digitalization
- Integrated Electronic Systems (research group)
- Embedded Electronics Engineering (M.Sc.)
- LTH Profile Area: Nanoscience and Semiconductor Technology
- LU Profile Area: Natural and Artificial Cognition
- publishing date
- 2023-05-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- ICC 2023 - IEEE International Conference on Communications
- pages
- 6 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- IEEE International Conference on Communications, ICC 2023
- conference location
- Rome, Italy
- conference dates
- 2023-05-28 - 2023-06-01
- external identifiers
-
- scopus:85178298856
- ISBN
- 978-1-5386-7463-5
- 978-1-5386-7462-8
- DOI
- 10.1109/ICC45041.2023.10278664
- language
- Unknown
- LU publication?
- yes
- id
- f13d0b4e-6ad9-40db-ad31-51aec0be1cf2
- date added to LUP
- 2023-11-15 11:46:41
- date last changed
- 2024-04-23 06:47:49
@inproceedings{f13d0b4e-6ad9-40db-ad31-51aec0be1cf2,
abstract = {{High-precision cellular-based localization is one of the key technologies for next-generation communication systems. In this paper, we investigate the potential of applying machine learning (ML) to a massive multiple-input multiple-output (MIMO) system to enhance localization accuracy. We analyze a new ML-based localization pipeline that has two parallel fully connected neural networks (FCNN). The first FCNN takes the instantaneous spatial covariance matrix to capture angular information, while the second FCNN takes the channel impulse responses to capture delay information. We fuse the estimated coordinates of these two FCNNs for further accuracy improvement. To test the localization algorithm, we performed an indoor measurement campaign with a massive MIMO testbed at 3.7 GHz. In the measured scenario, the proposed pipeline can achieve centimeter-level accuracy by combining delay and angular information.}},
author = {{Tian, Guoda and Yaman, Ilayda and Sandra, Michiel and Cai, Xuesong and Liu, Liang and Tufvesson, Fredrik}},
booktitle = {{ICC 2023 - IEEE International Conference on Communications}},
isbn = {{978-1-5386-7463-5}},
language = {{und}},
month = {{05}},
pages = {{3690--3695}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{High-Precision Machine-Learning Based Indoor Localization with Massive MIMO System}},
url = {{http://dx.doi.org/10.1109/ICC45041.2023.10278664}},
doi = {{10.1109/ICC45041.2023.10278664}},
year = {{2023}},
}