Semi-Supervised Deep Learning-Based Methods for Indoor Outdoor Detection

Detecting whether a mobile user is indoor or outdoor is an important issue which significantly impacts user behavior contextualization and mobile network resource management. Indoor Outdoor Detection (IOD) can be performed within mobile networks or in user terminals. Implementing IOD in mobile networks is interesting for operators as it is less costly, easier to deploy, and more energy efficient with centralized computing. This paper investigates hybrid/semi-supervised Deep Learning-based methods for detecting the environment of an active mobile phone user. They are based on both labeled and unlabeled large real radio data obtained from inside the network and from 3GPP signal measurements. We empirically evaluate the effectiveness of the semi-supervised learning methods using new real-time radio data, with partial ground truth information, gathered massively from multiple typical and diversified locations (indoor and outdoor) of mobile users. We also present an analysis of such schemes as compared to the existing supervised classification methods including SVM and Deep Learning.