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Deep convolutional neural networks for massive MIMO fingerprint-based positioning
Open accessAbstract
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, 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.
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Metadata
- Authors
- Vieira, J., Leitinger, E., Sarajlic, M., Li, X., & Tufvesson, F.
- Affiliations
- Lund University (SE)
- Venue
- 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 1-6
- OpenAlex
- W2624813472
- Cited by
- 186
- References
- 16
- Citations by year
- 2026: 8 · 2025: 7 · 2024: 25 · 2023: 23 · 2022: 20 · 2021: 32 · 2020: 24 · 2019: 30 · 2018: 17
- FWCI
- 27.47
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Millimeter-Wave Propagation and Modeling
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optical Wireless Communication Technologies
- Keywords
- fingerprint-based positioning, massive MIMO, convolutional neural networks, indoor positioning, deep convolutional neural networks
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