Deep Convolutional Neural Networks for Massive MIMO Fingerprint-Based Positioning
(2018) 28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, 2017- Abstract
- This paper provides an initial investigation on the application of convolutional neural networks (CNNs) for fingerprint-based positioning using measured massive MIMO channels. When represented in appropriate domains, measured massive MIMO channels have a sparse structure which can be efficiently learned by CNNs for positioning purposes. We evaluate the positioning accuracy of state-of-the-art CNNs with channel fingerprints generated from a channel model with a rich clustered structure: the COST 2100 channel model. We find that moderately deep CNNs can achieve fractional-wavelength positioning accuracies, provided that an enough representative data set is available for training.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4e3ace37-4634-414e-be93-a26fd6d96e5d
- author
- Vieira, Joao
LU
; Leitinger, Erik
LU
; Sarajlic, Muris
LU
; Li, Xuhong
LU
and Tufvesson, Fredrik
LU
- organization
- publishing date
- 2018-02-15
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2017.
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, 2017
- conference location
- Montreal, Canada
- conference dates
- 2017-10-08 - 2017-10-13
- external identifiers
-
- scopus:85045258482
- DOI
- 10.1109/PIMRC.2017.8292280
- language
- English
- LU publication?
- yes
- id
- 4e3ace37-4634-414e-be93-a26fd6d96e5d
- date added to LUP
- 2017-06-14 11:24:42
- date last changed
- 2022-05-10 08:25:17
@inproceedings{4e3ace37-4634-414e-be93-a26fd6d96e5d, abstract = {{This paper provides an initial investigation on the application of convolutional neural networks (CNNs) for fingerprint-based positioning using measured massive MIMO channels. When represented in appropriate domains, measured massive MIMO channels have a sparse structure which can be efficiently learned by CNNs for positioning purposes. We evaluate the positioning accuracy of state-of-the-art CNNs with channel fingerprints generated from a channel model with a rich clustered structure: the COST 2100 channel model. We find that moderately deep CNNs can achieve fractional-wavelength positioning accuracies, provided that an enough representative data set is available for training.}}, author = {{Vieira, Joao and Leitinger, Erik and Sarajlic, Muris and Li, Xuhong and Tufvesson, Fredrik}}, booktitle = {{28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2017.}}, language = {{eng}}, month = {{02}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Deep Convolutional Neural Networks for Massive MIMO Fingerprint-Based Positioning}}, url = {{http://dx.doi.org/10.1109/PIMRC.2017.8292280}}, doi = {{10.1109/PIMRC.2017.8292280}}, year = {{2018}}, }