A Funnel Fukunaga–Koontz Transform for Robust Indoor–Outdoor Detection Using Channel-State Information in 5G IoT Context

Abstract

The massive machine-type communication will be at the core of ambient connectivity, requiring for energy-efficient systems. Earlier studies highlighted the efficiency of positioning approaches based on channel-state information (CSI) in different environments. Many works limited the solution assessment to a single room in a fully indoor testbed. This article extends the application of CSI for indoor–outdoor detection on an unprecedented large area and considers mMTC-oriented long-term evolution and fifth-generation Internet of Things in the sub-GHz frequency band. Hinged on a novel long-term evolution protocol dedicated for machine-type communications, the results focus on a unique packet exchange with a single access point to save battery life and simplify deployment. The study evaluates different input features and investigates the target positioning accuracy for multiple unsupervised and supervised dimensionality reduction methods. We present a new dimension reduction scheme consisting of an unsupervised funnel on top of a supervised dimension reduction approach. Results show that the introduced Funnel Fukunaga–Koontz transform outperforms other dimension reduction approaches, regardless of the input features and the number of locations.