CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach

By Xuyu Wang, Lingjun Gao, Shiwen Mao. IEEE Internet of Things Journal, 2016

389 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Ocean Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3603664be73b939e40558fc5b8943a8b68f20273

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Shiwen Mao
2. Hamed Haddadi
3. Paul Patras
4. Chaoyun Zhang
5. Xuyu Wang
6. Yaohua Sun
7. Mugen Peng
8. Yuzhen Huang

In Ocean Engineering

1. Shiwen Mao
2. Paul Patras
3. Hamed Haddadi
4. Chaoyun Zhang
5. Xuyu Wang
6. Lingjun Gao
7. Mugen Peng
8. Yaohua Sun

In Signal Processing

1. Shiwen Mao
2. Paul Patras
3. Hamed Haddadi
4. Chaoyun Zhang
5. Xuyu Wang
6. Lingjun Gao
7. Mugen Peng
8. Yaohua Sun

Citations in the corpus

• A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation (2018, background): “For instance, in the physical layer, network processes that can be benefited include spatial spectrum sensing for cognitive radio and interference coordination in 5G, slow adaptive modulation and coding or channel estimation, beamforming, pilot decontamination in MIMO systems as described in [264] a”
• CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach (2016, methodology): “used as fingerprints to improve the performance of indoor local84 ization [7]–[11].”
• PhaseBeat: Exploiting CSI Phase Data for Vital Sign Monitoring with Commodity WiFi Devices (2017, background): “CSI reveals the channel characteristics experienced by the received signal such as the multipath effect, shadow fading, and distortion [26], [27].”
• Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “[242], [243] Activity and gesture recognition Cloud-based Stacked AE Cao et al.”
• A research agenda for augmented and virtual reality in architecture, engineering and construction (2020, methodology): “Further research should focus on using a combination of methods, for example by leveraging WiFi signals [30, 180], Bluetooth beacons [196], and magnetic fields [161].”
• Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues (2018, methodology): “In literatures [17], [122]–[125], [128]–[130], [133]–[135] focusing on machine learning based localization, training data is based on CSI data, RSSI data or some channel parameters.”