TY - GEN
T1 - Gaussian Models for CSI Fingerprinting in Practical Indoor Environment Identification
AU - Rocamora, Josyl Mariela
AU - Ho, Ivan Wang Hei
AU - Mak, Man Wai
N1 - Funding Information:
The work of I. W.-H. Ho was supported in part by the Research Impact Fund (Project No. R5007-18) established under the University Grant Committee (UGC) of the Hong Kong Special Administrative Region (HKSAR), China. The work of J. Rocamora was supported in part by the General Research Fund (Project No. 15201118) established under the UGC of the HKSAR, China.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - It is not uncommon to experience highly dynamic channels in indoor environments due to time-varying signals as well as moving reflectors and scatterers. This greatly affects the performance of wireless sensing systems that use received signal strength indicator (RSSI) and channel state information (CSI) fingerprints for indoor positioning and event detection. Solutions to this dynamic channel problem often involve laborintensive database maintenance and customized hardware. With this, we present Gaussian models that can withstand temporal and environmental dynamics in practical indoor environments using off-the-shelf devices in this paper. Although systems employing Gaussian models have been previously proposed in the literature, most systems use RSSI instead of CSI to represent the wireless channel. By using a Gaussian distribution to model CSI fingerprints, which offer more abundant information regarding the channel dynamics than RSSI, we can exploit the variance inherent in the wireless channels. Our experiments demonstrate that the Gaussian classifier incurs minimal delay of less than 4 seconds and achieves high classification accuracy compared to other techniques. In particular, it achieves up to 50% and 150% performance improvement over the time-reversal resonating strength (TRRS) and the support vector machines (SVM) methods, respectively.
AB - It is not uncommon to experience highly dynamic channels in indoor environments due to time-varying signals as well as moving reflectors and scatterers. This greatly affects the performance of wireless sensing systems that use received signal strength indicator (RSSI) and channel state information (CSI) fingerprints for indoor positioning and event detection. Solutions to this dynamic channel problem often involve laborintensive database maintenance and customized hardware. With this, we present Gaussian models that can withstand temporal and environmental dynamics in practical indoor environments using off-the-shelf devices in this paper. Although systems employing Gaussian models have been previously proposed in the literature, most systems use RSSI instead of CSI to represent the wireless channel. By using a Gaussian distribution to model CSI fingerprints, which offer more abundant information regarding the channel dynamics than RSSI, we can exploit the variance inherent in the wireless channels. Our experiments demonstrate that the Gaussian classifier incurs minimal delay of less than 4 seconds and achieves high classification accuracy compared to other techniques. In particular, it achieves up to 50% and 150% performance improvement over the time-reversal resonating strength (TRRS) and the support vector machines (SVM) methods, respectively.
KW - Channel State Information (CSI)
KW - Clustering
KW - Gaussian Classifiers
KW - Wireless Sensing
UR - http://www.scopus.com/inward/record.url?scp=85100396618&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM42002.2020.9322189
DO - 10.1109/GLOBECOM42002.2020.9322189
M3 - Conference article published in proceeding or book
AN - SCOPUS:85100396618
T3 - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
BT - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
CY - Taipei, Taiwan
T2 - 2020 IEEE Global Communications Conference, GLOBECOM 2020
Y2 - 7 December 2020 through 11 December 2020
ER -