Neural 5G Indoor Localization with IMU Supervision

arXiv:2402.09948v1 Announce Type: cross
Abstract: Radio signals are well suited for user localization because they are ubiquitous, can operate in the dark and maintain privacy. Many prior works learn mappings between channel state information (CSI) and position fully-supervised. However, that approach relies on position labels which are very expensive to acquire. In this work, this requirement is relaxed by using pseudo-labels during deployment, which are calculated from an inertial measurement unit (IMU). We propose practical algorithms for IMU double integration …

abstract arxiv cs.lg eess.sp information labels learn localization prior privacy radio state supervision type