TY - GEN
T1 - Network-side positioning of cellular-band devices with minimal effort
AU - Chakraborty, Ayon
AU - Ortiz, Luis E.
AU - Das, Samir R.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/8/21
Y1 - 2015/8/21
N2 - We address the problem of network-side localization where cellular operators are interested in localizing cellular devices by means of signal strength measurements alone. While fingerprinting-based approaches have been used recently to address this problem, they require significant amount of geo-tagged ('labeled') measurement data that is expensive for the operator to collect. Our goal is to use semi-supervised and unsupervised machine learning techniques to reduce or eliminate this effort without compromising the accuracy of localization. Our experimental results in a university campus (6 sq. km) demonstrate that sub-100m median localization accuracy is achievable with very little or no labeled data so long as enough training is possible with 'unlabeled' measurements. This provides an opportunity for the operator to improve the model with time. We present extensive analysis of the error characteristics to gain insight and improve performance, including understanding spatial properties and developing confidence measures.
AB - We address the problem of network-side localization where cellular operators are interested in localizing cellular devices by means of signal strength measurements alone. While fingerprinting-based approaches have been used recently to address this problem, they require significant amount of geo-tagged ('labeled') measurement data that is expensive for the operator to collect. Our goal is to use semi-supervised and unsupervised machine learning techniques to reduce or eliminate this effort without compromising the accuracy of localization. Our experimental results in a university campus (6 sq. km) demonstrate that sub-100m median localization accuracy is achievable with very little or no labeled data so long as enough training is possible with 'unlabeled' measurements. This provides an opportunity for the operator to improve the model with time. We present extensive analysis of the error characteristics to gain insight and improve performance, including understanding spatial properties and developing confidence measures.
UR - https://www.scopus.com/pages/publications/84954226260
U2 - 10.1109/INFOCOM.2015.7218669
DO - 10.1109/INFOCOM.2015.7218669
M3 - Conference contribution
AN - SCOPUS:84954226260
T3 - Proceedings - IEEE INFOCOM
SP - 2767
EP - 2775
BT - 2015 IEEE Conference on Computer Communications, IEEE INFOCOM 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015
Y2 - 26 April 2015 through 1 May 2015
ER -