Exploiting wavefront curvature enables localization with limited infrastructure and hardware complexity. With the introduction of reconfigurable intelligent surfaces (RISs), new opportunities arise, in particular when the RIS is functioning as a lens receiver. We investigate the localization of a transmitter using a RIS-based lens in close proximity to a single receive antenna element attached to reception radio frequency chain. We perform a Fisher information analysis, evaluate the impact of different lens configurations, and propose a two-stage localization algorithm. Our results indicate that positional beamforming can lead to better performance when a priori location information is available, while random beamforming is preferred when a priori information is lacking. Our simulation results for a moderate size lens operating at 28 GHz showcased that decimeter-level accuracy can be attained within 3 meters to the lens.

We consider the problem of joint three-dimensional localization and synchronization for a single-input single-output (SISO) multi-carrier system in the presence of a reconfigurable intelligent surface (RIS), equipped with a uniform planar array. First, we derive the Cramér-Rao bounds (CRBs) on the estimation error of the channel parameters, namely, the angleof-departure (AOD), composed of azimuth and elevation, from RIS to the user equipment (UE) and times-of-arrival (TOAs) for the path from the base station (BS) to UE and BS-RIS-UE reflection. In order to avoid high-dimensional search over the parameter space, we devise a low-complexity estimation algorithm that performs two 1D searches over the TOAs and one 2D search over the AODs. Simulation results demonstrate that the considered RIS-aided wireless system can provide submeterlevel positioning and synchronization accuracy, materializing the positioning capability of Beyond 5G networks even with singleantenna BS and UE. Furthermore, the proposed estimator is shown to attain the CRB at a wide interval of distances between UE and RIS. Finally, we also investigate the scaling of the position error bound with the number of RIS elements.

Reconfigurable intelligent surfaces (RISs) have the potential to enable user localization in scenarios where traditional approaches fail. Building on prior work in single-antenna RIS-enabled localization, we investigate the potential to exploit wavefront curvature in geometric near-field conditions. Via a Fisher information analysis, we demonstrate that while near-field improves localization accuracy mostly at short distances when the line-of-sight (LoS) path is present, it could still provide reasonable performance when this path is blocked by relying on a single RIS reflection. After deriving and illustrating the corresponding position error bounds as a function of key operating parameters, we discuss practical system approaches that could enable better LoS-to-NLoS positioning continuity in harsh environments.

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Reconfigurable intelligent surface (RIS), operating as a lens or a reflector, is set to be a revolutionary technology in the 6th generation of wireless systems. With most works that consider RISs as reflectors, the RIS provides a non lineof-sight (NLOS) path between the base station and the user.In this letter, we study the application of RIS in a multi-user passive localization scenario, where RIS is mounted on the user side, providing NOLS paths between a transmitter and multiple asynchronous receivers. We show that user's 3D position can be estimated with submeter accuracy in a large area around the transmitter, using LOS and NLOS time-of-arrival measurements at the receivers. We do so, by developing the signal model, deriving the Cramér-Rao bounds, and devising an estimator that attains the bounds. Furthermore, by properly adjusting the RIS phase profiles, we circumvent inter-path interference.