E-Loc: Enhanced CSI Fingerprinting Localization for massive Machine-Type Communications in Wi-Fi Ambient Connectivity

A location-based service in the massive machine-type wireless communications (mMTC) must respect different requirements such as a minimal energy consumption at the target device or estimating the location in ambient connectivity. The solutions in mMTC must then consider localization approaches that provide target locations with few transmitted signals and with the support of only one anchor gateway. It is also major to use a relevant input data that manages the complex radio propagation mediums. In indoor environments, a solution builds on fingerprinting approach based on the channel state information (CSI) between the target device and a single anchor gateway. This paper presents a novel CSI fingerprinting localization method, E-Loc for mMTC dedicated to indoor systems in the Wi-Fi ambient connectivity context. E-Loc architecture is based on a convolutional neural network implementing inception models with an innovative design. CSI has been collected in a complex indoor environment, post-processed to handle the transmit power diversity, phase and timing offsets and fed to E-Loc. In various spatial distributions of training locations, E-Loc outperforms other tested solutions with a 99% confidence level for localization errors around 5 meters.