  Fine-grained indoor localization has attracted attention recently because of
the rapidly growing demand for indoor location-based services (ILBS).
Specifically, massive (large-scale) multiple-input and multiple-output (MIMO)
systems have received increasing attention due to high angular resolution. This
paper presents an indoor localization testbed based on a massive MIMO
orthogonal frequency-division multiplexing (OFDM) system, which supports
physical-layer channel measurements. Instead of exploiting channel state
information (CSI) directly for localization, we focus on positioning from the
perspective of multipath components (MPCs), which are extracted from the CSI
through the space-alternating generalized expectation-maximization (SAGE)
algorithm. On top of the available MPCs, we propose a generalized
fingerprinting system based on different single-metric and hybrid-metric
schemes. We evaluate the impact of the varying antenna topologies, the size of
the training set, the number of antennas, and the effective signal-to-noise
ratio (SNR). The experimental results show that the proposed fingerprinting
method can achieve centimeter-level positioning accuracy with a relatively
small training set. Specifically, the distributed uniform linear array obtains
the highest accuracy with about 1.63-2.5-cm mean absolute errors resulting from
the high spatial resolution.
