The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
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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Cites
Fundamentals of Statistical Signal Processing: Estimation Theory
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Massive MIMO for next generation wireless systems
Larsson, E. G., Edfors, O., Tufvesson, F., & Marzetta, T. L. (2014) · IEEE Communications Magazine · doi:10.1109/mcom.2014.6736761 · Cited by 6,797
Self-citationNoncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas
Marzetta, T. L. (2010) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2010.092810.091092 · Cited by 6,571
The COST 2100 MIMO channel model
Liu, L., Oestges, C., Poutanen, J., Haneda, K., Vainikainen, P., Quitin, F., Tufvesson, F., & Doncker, P. D. (2012) · IEEE Wireless Communications · doi:10.1109/mwc.2012.6393523 · Cited by 703
Self-citationDirect Localization for Massive MIMO
Garcia, N., Wymeersch, H., Larsson, E. G., Haimovich, A. M., & Coulon, M. (2017) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2017.2666779 · Cited by 381
On Complex Valued Convolutional Neural NetworksPreprint
Guberman, N. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.09046 · Cited by 164
Massive MIMO channels — Measurements and models
Gao, X., Tufvesson, F., & Edfors, O. (2013) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2013.6810277 · Cited by 156
Self-citationA Time-Reversal Paradigm for Indoor Positioning System
Wu, Z.-H., Han, Y., Chen, Y., & Liu, K. J. R. (2015) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2015.2397437 · Cited by 150
Fingerprinting-Based Positioning in Distributed Massive MIMO Systems
Savić, V., & Larsson, E. G. (2015) · IEEE Vehicular Technology Conference · doi:10.1109/vtcfall.2015.7390953 · Cited by 142
Zhu, M., Eriksson, G., & Tufvesson, F. (2013) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2013.010413.120620 · Cited by 99
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Luong, N. C., Hoang, D. T., Gong, S., Niyato, D. T., Wang, P., Liang, Y., & Kim, D. I. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2916583 · Cited by 2,181
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,913
Massive MIMO is a reality—What is next?
Björnson, E., Sanguinetti, L., Wymeersch, H., Hoydis, J., & Marzetta, T. L. (2019) · Digital Signal Processing · doi:10.1016/j.dsp.2019.06.007 · Cited by 879
Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?
Zappone, A., Renzo, M. D., & Debbah, M. A. (2019) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2924010 · Cited by 703
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future
Nawaz, S. J., Sharma, S. K., Wyne, S., Patwary, M. N., & Asaduzzaman, M. (2019) · IEEE Access · doi:10.1109/access.2019.2909490 · Cited by 658
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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