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On Deep Learning-based Massive MIMO Indoor User Localization

Abstract: 

We examine the usability of deep neural networks for multiple-input multiple-output (MIMO) user positioning solely based on the orthogonal frequency division multiplex (OFDM) complex channel coefficients. In contrast to other indoor positioning systems (IPSs), the proposed method does not require any additional piloting overhead or any other changes in the communications system itself as it is deployed on top of an existing OFDM MIMO system. Supported by actual measurements, we are mainly interested in the more challenging non-line of sight (NLoS) scenario. However, gradient descent optimization is known to require a large amount of data-points for training, i.e., the required database would be too large when compared to conventional methods. Thus, we propose a two-step training procedure, with training on simulated line of sight (LoS) data in the first step, and finetuning on measured NLoS positions in the second step. This turns out to reduce the required measured training positions and thus, reduces the effort for data acquisition.

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Paper Details

Authors:
Maximilian Arnold, Stephan ten Brink
Submitted On:
21 June 2018 - 11:57am
Short Link:
Type:
Poster
Event:
Presenter's Name:
Sebastian Dörner
Paper Code:
spawc18001
Document Year:
2018
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Document Files

spawc_positioning_poster.pdf

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[1] Maximilian Arnold, Stephan ten Brink, "On Deep Learning-based Massive MIMO Indoor User Localization", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3286. Accessed: Apr. 22, 2019.
@article{3286-18,
url = {http://sigport.org/3286},
author = {Maximilian Arnold; Stephan ten Brink },
publisher = {IEEE SigPort},
title = {On Deep Learning-based Massive MIMO Indoor User Localization},
year = {2018} }
TY - EJOUR
T1 - On Deep Learning-based Massive MIMO Indoor User Localization
AU - Maximilian Arnold; Stephan ten Brink
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3286
ER -
Maximilian Arnold, Stephan ten Brink. (2018). On Deep Learning-based Massive MIMO Indoor User Localization. IEEE SigPort. http://sigport.org/3286
Maximilian Arnold, Stephan ten Brink, 2018. On Deep Learning-based Massive MIMO Indoor User Localization. Available at: http://sigport.org/3286.
Maximilian Arnold, Stephan ten Brink. (2018). "On Deep Learning-based Massive MIMO Indoor User Localization." Web.
1. Maximilian Arnold, Stephan ten Brink. On Deep Learning-based Massive MIMO Indoor User Localization [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3286