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

TL;DR

This paper introduces a novel Massive MIMO channel sounder and dataset, enabling deep learning-based indoor positioning with high accuracy in both line-of-sight and non-line-of-sight scenarios.

Contribution

The paper presents a new channel sounder architecture and provides an open dataset for channel impulse responses, facilitating research in deep learning-based indoor positioning.

Findings

Achieved better than 75 cm accuracy in LoS positioning

Demonstrated comparable accuracy in NLoS conditions

Provided a validated dataset for future research

Abstract

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 multiantenna and multi-subcarrier channel state information (CSI) at different frequency bands, antenna geometries and propagation environments. The channel sounder has been verified by evaluation of 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 we do achieve an accuracy better than 75 cm for line of sight (LoS), as is comparable to state-of-the-art conventional positioning techniques, but also obtain similar precision for the 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.