2021/04/16 by Arthur Gassner, Gassner, Arthur, Claudiu Musat +5 · 1 citation
Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Indoor and Outdoor Localization Technologies #Machine Learning (cs.LG) #Millimeter-Wave Propagation and Modeling #Signal Processing (eess.SP) #Speech and Audio Processing #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2104.07963
openalex publication_date 2021/04/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many applications require accurate indoor localization. Fingerprint-based localization methods propose a solution to this problem, but rely on a radio map that is effort-intensive to acquire. We automate the radio map acquisition phase using a software-defined radio (SDR) and a wheeled robot. Furthermore, we open-source a radio map acquired with our automated tool for a 3GPP Long-Term Evolution (LTE) wireless link. To the best of our knowledge, this is the first publicly available radio map containing channel state information (CSI). Finally, we describe first localization experiments on this radio map using a convolutional neural network to regress for location coordinates.