DeepFi: Deep learning for indoor fingerprinting using channel state information
Abstract
With the fast growing demand of location-based services in indoor environments, indoor positioning based on fingerprinting has attracted a lot of interest due to its high accuracy. In this paper, we present a novel deep learning based indoor fingerprinting system using Channel State Information (CSI), which is termed DeepFi. Based on three hypotheses on CSI, the DeepFi system architecture includes an off-line training phase and an on-line localization phase. In the off-line training phase, deep learning is utilized to train all the weights as fingerprints. Moreover, a greedy learning algorithm is used to train all the weights layer-by-layer to reduce complexity. In the on-line localization phase, we use a probabilistic method based on the radial basis function to obtain the estimated location. Experimental results are presented to confirm that DeepFi can effectively reduce location error compared with three existing methods in two representative indoor environments.
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The running time per location for the SDNNLoc is almost the same as the DeepFi system, where both use the DNN based technique.
“The running time per location for the SDNNLoc is almost the same as the DeepFi system, where both use the DNN based technique.”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [72]
The SDNNLoc system is more accurate than the other two, with fewer location errors.
“The SDNNLoc system is more accurate than the other two, with fewer location errors.”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [72]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841 · see citing works
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Wireless Sensing for Human Activity: A Survey
Liu, J., Liu, H., Chen, Y., Wang, Y., & Wang, C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2934489 · Cited by 414 · see citing works
Embedded Intelligence on FPGA: Survey, Applications and Challenges
Seng, K. P., Lee, P. J., & Ang, L. M. (2021) · Electronics · doi:10.3390/electronics10080895 · Cited by 105 · see citing works
“The running time per location for the SDNNLoc is almost the same as the DeepFi system, where both use the DNN based technique.”
MethodSWiBluX: Multi-Sensor Deep Learning Fingerprint for Precise Real-Time Indoor Tracking
Belmonte-Hernandez, A., Hernandez-Penaloza, G., Martin Gutierrez, D., & Alvarez, F. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2892590 · Cited by 53 · see citing works
A Wi-Fi-Based Wireless Indoor Position Sensing System with Multipath Interference Mitigation
Xie, T., Jiang, H., Zhao, X., & Zhang, C. (2019) · Sensors · doi:10.3390/s19183983 · Cited by 32 · see citing works
- Authors
- Wang, X., Gao, L., Mao, S., & Pandey, S.
- Affiliations
- Auburn University (US)
- Cisco Systems (United States) (US)
- Venue
- 2015 IEEE Wireless Communications and Networking Conference (WCNC)
- Publisher
- IEEE
- Published
- 2015-03
- Pages
- 1666-1671
- ISSN
- 1525-3511 (ISSN-L)
- OpenAlex
- W2240192984
- Cited by
- 381
- References
- 2
- Citations by year
- 2026: 23 · 2025: 31 · 2024: 25 · 2023: 45 · 2022: 33 · 2021: 39 · 2020: 54 · 2019: 71 · 2018: 29 · 2017: 16 · 2016: 13 · 2015: 1 · 2014: 1
- FWCI
- 24.36
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Wireless Communications and Networking Conference (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optical Wireless Communication Technologies
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- indoor positioning, channel state information, deep learning, greedy learning, radial basis functions, location-based services, location error reduction
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