  Reconfigurable Intelligent Surfaces (RISs) are announced as a truly
transformative technology, capable of smartly shaping wireless environments to
optimize next-generation communication networks. Among their numerous foreseen
applications, Reflective RISs (RRISs) have been shown theoretically beneficial
not only to enable wireless localization through controlled multipath in
situations where conventional systems would fail (e.g., with too few available
base stations (BSs) and/or under radio blockages) but also to locally boost
accuracy on demand (typically, in regions close to the surface). In this paper,
leveraging a dedicated frequency-domain mmWave indoor channel sounding
campaign, we present the first experimental evidences of such benefits, by
emulating offline simple RIS-aided single-BS positioning scenarios including
line-of-sight (LoS) and non-line-of-sight (NLoS), single-RIS and multi-RIS, and
multiple user equipment (UE) locations, also by considering various
combinations of estimated multipath parameters (e.g., delays, Angle of
Departure (AoD) or gains) as inputs to basic Least Squares (LS) solvers.
Despite their simplicity, these preliminary proof-of-concept validations show
concretely how and when RIS-reflected paths could contribute to enhance
localization performance.
