Novel Massive MIMO Channel Sounding Data applied to Deep Learning-based Indoor Positioning

With a significant increase in area throughput, Massive MIMO has become an enabling technology for fifth generation (5G) wireless mobile communication systems. Although prototypes were built, an openly available dataset for channel impulse responses to verify assumptions, e.g., regarding channel sparsity, is not yet available. In this paper, we introduce a novel channel sounder architecture capable of measuring multi-antenna and multi-subcarrier channel state information (CSI) at different frequency bands, antenna geometries and propagation environments. The channel sounder has been verified by evaluating channel data from first measurements. Such datasets can be used to study various deep-learning (DL) techniques in different applications, e.g., for indoor user positioning in three dimensions, as is done in this paper. Not only do we achieve an accuracy better than 75cm for line of sight (LoS), as is comparable to stateof-the-art conventional positioning techniques, but also obtain similar precision for the much more challenging case of non-line of sight (NLoS). Further extensive indoor/outdoor measurement campaigns will provide a more comprehensive open CSI dataset, tagged with positions, for the scientific community to further test various algorithms.