CSI-based Positioning in Massive MIMO systems using Convolutional Neural Networks

Sibren De Bast, Adrea P. Guevara, Sofie Pollin
11/26/2019
eess.SP

Official code: sibrendebast/MaMIMO-CSI-positioning-using-CNNs (35 stars, GPL-3.0, last commit 2022-01-12).

Abstract

This paper studies the performance of a user positioning system using Channel State Information (CSI) of a Massive MIMO (MaMIMO) system. To infer the position of the user from the CSI, a Convolutional Neural Network is designed and evaluated through a novel dataset. This dataset contains indoor MaMIMO CSI measurements using three different antenna topologies, covering a 2.5 m by 2.5 m indoor area. We show that we can train a Convolutional Neural Network (CNN) model to estimate the position of a user inside this area with a mean error of less than half a wavelength. Moreover, once the model is trained on a given scenario and antenna topology, Transfer Learning is used to repurpose the acquired knowledge towards another scenario with significantly different antenna topology and configuration. Our results show that it is possible to further train the CNN using only a small amount of extra labelled samples for the new topology. This transfer learning approach is able to reach accurate results, paving the road to a practical CSI-based positioning system powered by CNNs.

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Code Implementations(1)

sibrendebast/MaMIMO-CSI-positioning-using-CNNsOfficial100%
GPL-3.0

This repository contains the source code used for the results in "CSI-based Positioning in Massive MIMO systems using Convolutional Neural Networks".

359PythonOct 28, 20194 years agoGPL-3.0

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Cite this paper

@article{bast2019csibased,
  title  = {CSI-based Positioning in Massive MIMO systems using Convolutional Neural Networks},
  author = {Sibren De Bast and Adrea P. Guevara and Sofie Pollin},
  year   = {2019},
  eprint = {1911.11523},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1911.11523}
}

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