Network-side positioning of cellular-band devices with minimal effort

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.