  Channel state information (CSI)-based fingerprinting via neural networks
(NNs) is a promising approach to enable accurate indoor and outdoor positioning
of user equipments (UEs), even under challenging propagation conditions. In
this paper, we propose a positioning pipeline for wireless LAN MIMO-OFDM
systems which uses uplink CSI measurements obtained from one or more
unsynchronized access points (APs). For each AP receiver, novel features are
first extracted from the CSI that are robust to system impairments arising in
real-world transceivers. These features are the inputs to a NN that extracts a
probability map indicating the likelihood of a UE being at a given grid point.
The NN output is then fused across multiple APs to provide a final position
estimate. We provide experimental results with real-world indoor measurements
under line-of-sight (LoS) and non-LoS propagation conditions for an 80MHz
bandwidth IEEE 802.11ac system using a two-antenna transmit UE and two AP
receivers each with four antennas. Our approach is shown to achieve
centimeter-level median distance error, an order of magnitude improvement over
a conventional baseline.
