The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach
Open accessAbstract
With the fast-growing demand of location-based services in indoor environments, indoor positioning based on fingerprinting has attracted significant 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 offline training phase and an online localization phase. In the offline training phase, deep learning is utilized to train all the weights of a deep network as fingerprints. Moreover, a greedy learning algorithm is used to train the weights layer by layer to reduce complexity. In the online 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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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
RADAR: an in-building RF-based user location and tracking system
Bahl, P., & Padmanabhan, V. N. (2002) · Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064) · doi:10.1109/infcom.2000.832252 · Cited by 8,380
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
The Horus WLAN location determination system
Youssef, M., & Agrawala, A. (2005) · Proceedings of the 3rd international conference on Mobile systems, applications, and services · doi:10.1145/1067170.1067193 · Cited by 1,653
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Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems
Ye, H., Li, G. Y., & Juang, B.-H. (2018) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2017.2757490 · Cited by 2,000
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 1,768
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
Sun, Y., Peng, M., Zhou, Y., Huang, Y., & Mao, S. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2924243 · Cited by 678
Self-citationThirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Wang, J., Jiang, C., Zhang, H., Ren, Y., Chen, K.-C., & Hanzo, L. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2965856 · Cited by 486
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
Metadata
- Authors
- Wang, X., Gao, L., Mao, S., & Pandey, S.
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2016
- Volume
- 66
- Issue
- 1
- Pages
- 1-1
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W2309512289
- Cited by
- 1,106
- References
- 4
- FWCI
- 53.05
- Type
- journal article
- Language
- en
- Open access
- green
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