Constrained RIS Phase Profile Optimization and Time Sharing for Near-field Localization

Abstract

In the context of single-base station (BS) nonline-of-sight (NLoS) single-epoch localization with the aid of a reflective reconfigurable intelligent surface (RIS), this paper introduces a novel three-step algorithm that jointly estimates the position and velocity of a mobile user equipment (UE), while compensating for the Doppler effects observed in near-field (NF) at the RIS elements over the short transmission duration of a sequence of downlink (DL) pilot symbols. First, a low-complexity initialization procedure is proposed, relying in part on far-field (FF) approximation and a static user assumption. Then, an alternating optimization procedure is designed to iteratively refine the velocity and position estimates, as well as the channel gain. The refinement routines leverage small angle approximations and the linearization of the RIS response, accounting for both NF and mobility effects. We evaluate the performance of the proposed algorithm through extensive simulations under diverse operating conditions with regard to signal-to-noise ratio (SNR), UE mobility, uncontrolled multipath and RIS-UE distance. Our results reveal remarkable performance improvements over the stateof-the-art (SoTA) mobility-agnostic benchmark algorithm, while indicating convergence of the proposed algorithm to respective theoretical bounds on position and velocity estimation.

Performance of RIS-Aided Nearfield Localization under Beams Approximation from Real Hardware Characterization

“…Furthermore, distributing the positions in a smaller uncertainty sphere yields a PEB close to that projected onto sets K1 and K1 in the localization-optimal design only at short distances but then degrades quickly as the RIS-UE distance increases. On the other hand, using the bigger sphere gives the opposite behavior, this is inline with the results presented in [13].…”

“…1). Aiming more specifically at improving NF localization performance in [13], it has been shown that such optimization would necessitate the use of four distinct types of beams at the reflective RIS (i.e., one steering beam and its three derivatives with respect to (w.r.t.) spherical coordinates).…”

“…Although the previous approach is generic and applicable to any type of RISs or beams, as a concrete and practical study case, we herein consider applying it into the specific context of RIS-aided NF localization, which somehow requires combining particular RIS beams to reach optimality. Localization-optimal RIS phase configurations can indeed be determined through position error bound (PEB) optimization, assuming prior information about the UE [12,13]. The aim is hence to improve positioning performance in non-line-of-sight (NLoS) situations, while relying on a unique RIS-reflected path (i.e., as estimated at the UE) over a sequence of single-input-single-output (SISO) downlink transmissions [14,15] (See Fig.…”

RIS-aided Localization under Pixel Failures

“…Taking into account the scatterers in the links from BS to RIS and from RIS to UE, [13] employs compressive sampling and optimized RIS configurations to achieve high accuracy. Moreover, RIS phase profile optimization for localization in NF has been proposed in [17] based on a combination of positional and derivative beams, which reveals considerable improvements over standard positional beams. The work in [18] tackles a similar problem for a linear RIS.…”

Channel Model Mismatch Analysis for XL-MIMO Systems from a Localization Perspective

“…Some recent works investigate error bounds and develop localization algorithms in NF scenarios considering both SNS and SWM [13], [14], or only SWM [15]. In particular, tracking with filter evaluations [13], compressive sensing-based algorithm [15] and constrained RIS profile optimization [14] are discussed for NF localization. However, the complexity of the NF models precludes the development of scalable algorithms for XL-MIMO systems.…”