Browsing by Subject "Reconfigurable intelligent surfaces"

Now showing 1 - 5 of 5

Open Access
Intelligent reflecting surfaces for visible light positioning based on received power measurements
(IEEE, 2024-09) Kökdoğan, Furkan; Gezici, Sinan
In this paper, we formulate and analyze a received power based position estimation problem for visible light positioning (VLP) systems in presence of intelligent reflecting surfaces (IRSs). In the proposed problem formulation, a visible light communication (VLC) receiver collects signals from a number of light emitting diode (LED) transmitters via line-of-sight (LOS) paths and/or via reflections from IRSs. We derive the Cramér--Rao lower bound (CRLB) expression and the maximum likelihood (ML) estimator for generic three-dimensional positioning in the presence of IRSs with arbitrary configurations. In addition, we consider the problem of optimizing the orientations of IRSs when line-of-sight (LOS) paths are blocked, and propose an optimal adjustment approach for maximizing the received powers from IRSs based on analytic expressions, which can be solved in closed form or numerically. Since the optimal IRS orientations depend on the actual position of the VLC receiver, an N-step localization algorithm is proposed to perform adjustment of IRS orientations in the absence of any prior knowledge about the position of the VLC receiver. Performance of the proposed approach is evaluated via simulations and compared against the CRLB. It is deduced that although IRSs do no provide critical improvements in positioning accuracy in the presence of LOS signals from a sufficient number of LED transmitters, they can be very important in achieving accurate positioning when all or most of LOS paths are blocked
Open Access
RIS-aided localization under pixel failures
(IEEE, 2024-08) Ozturk, Cuneyd; Keskin, Musa Furkan; Sciancalepore, Vincenzo; Wymeersch, Henk; Gezici, Sinan
Reconfigurable intelligent surfaces (RISs) hold great potential as one of the key technological enablers for beyond-5G wireless networks, improving localization and communication performance under line-of-sight (LoS) blockage conditions. However, hardware imperfections might cause RIS elements to become faulty, a problem referred to as pixel failures, which can constitute a major showstopper especially for localization. In this paper, we investigate the problem of RIS-aided localization of a user equipment (UE) under LoS blockage in the presence of RIS pixel failures, considering the challenging single-input single-output (SISO) scenario. We first explore the impact of such failures on accuracy through misspecified Cramér-Rao bound (MCRB) analysis, which reveals severe performance loss with even a small percentage of pixel failures. To remedy this issue, we develop two strategies for joint localization and failure diagnosis (JLFD) to detect failing pixels while simultaneously locating the UE with high accuracy. The first strategy relies on $\ell _{1}$-regularization through exploitation of failure sparsity. The second strategy detects the failures one-by-one by solving a multiple hypothesis testing problem at each iteration, successively enhancing localization and diagnosis accuracy. Simulation results show significant performance improvements of the proposed JLFD algorithms over the conventional failure-agnostic benchmark, enabling successful recovery of failure-induced performance degradations.
Open Access
RIS-aided near-field localization under phase-dependent amplitude variations
(Institute of Electrical and Electronics Engineers , 2023-08-14) Ozturk, C.; Keskin, M. F.; Wymeersch, H.; Gezici, Sinan
We investigate the problem of reconfigurable intelligent surface (RIS)-aided near-field localization of a user equipment (UE) served by a base station (BS) under phase-dependent amplitude variations at each RIS element. Through a misspecified Cramér-Rao bound (MCRB) analysis and a resulting lower bound (LB) on localization, we show that when the UE is unaware of amplitude variations (i.e., assumes unit-amplitude responses), severe performance penalties can arise, especially at high signal-to-noise ratios (SNRs). Leveraging Jacobi-Anger expansion to decouple range-azimuth-elevation dimensions, we develop a low-complexity approximated mismatched maximum likelihood (AMML) estimator, which is asymptotically tight to the LB. To mitigate performance loss due to model mismatch, we propose to jointly estimate the UE location and the RIS amplitude model parameters. The corresponding Cramér-Rao bound (CRB) is derived, as well as an iterative refinement algorithm, which employs the AMML method as a subroutine and alternatingly updates individual parameters of the RIS amplitude model. Simulation results indicate fast convergence and performance close to the CRB. The proposed method can successfully recover the performance loss of the AMML under a wide range of RIS parameters and effectively calibrate the RIS amplitude model online with the help of a user that has an a-priori unknown location.
Open Access
RIS-Aided NLoS monostatic sensing under mobility and angle-doppler coupling
(IEEE, 2024-06-03) Ercan, Mahmut Kemal; Keskin, Musa Furkan; Gezici, Sinan; Wymeersch, Henk
We investigate the problem of reconfigurable intelligent surface (RIS)-aided monostatic sensing of a mobile target under line-of-sight (LoS) blockage considering a single-antenna, full-duplex, and dual-functional radar-communications base station (BS). For the purpose of target detection and delay/Doppler/angle estimation, we derive a detector based on the generalized likelihood ratio test (GLRT), which entails a high-dimensional parameter search and leads to angle-Doppler coupling. To tackle these challenges, we propose a two-step algorithm for solving the GLRT detector/estimator in a low-complexity manner, accompanied by a RIS phase profile design tailored to circumvent the angle-Doppler coupling effect. Simulation results verify the effectiveness of the proposed algorithm, demonstrating its convergence to theoretical bounds and its superiority over state-of-the-art mobility-agnostic benchmarks.
Open Access
Visible light positioning under hardware impairments
(2024-07) Iddrisu, Issifu
In this thesis, we examine two distinct research problems in visible light positioning (VLP). Specifically, we explore the impact of lacking knowledge about luminous flux degradation in light-emitting diodes (LEDs) and mismatched orientations for the elements of intelligent reflecting surfaces (IRSs) on the performance of VLP systems. In the first part of the thesis, the position estimation problem based on received power measurements is investigated for visible light systems in the presence of luminous flux degradation of LEDs. When the receiver is unaware of this degradation and performs position estimation accordingly, there exists a mismatch between the true model and the assumed model. For this scenario, the misspecified Cram´er-Rao bound (MCRB) and the mismatched maximum likelihood (MML) estimator are derived to quantify the performance loss due to this model mismatch. Also, the Cram´er-Rao lower bound (CRB) and the maximum likeli-hood (ML) estimator are derived when the receiver knows the degradation formula for the LEDs but does not know the decay rate parameter in that formula. In addition, in the presence of full knowledge about the degradation formula and the decay rate parameters, the CRB and the ML estimator are obtained to specify the best achievable performance. By evaluating the theoretical limits and the estimators in these three scenarios, we reveal the effects of the information about the LED degradation model and the decay rate parameters on position estimation performance. It is shown that the model mismatch can result in significant degradation in localization performance at high signal-to-noise ratios, which can be compensated by conducting joint position and decay rate parameter estimation. Accurate localization can be performed in visible light systems in non-line-of-sight (NLOS) scenarios by utilizing IRSs, which are commonly in the form of mirror arrays with adjustable orientations. When signals transmitted from LEDs are reflected from IRSs and collected by a receiver, the position of the receiver can be estimated based on power measurements by utilizing the known parameters of the LEDs and IRSs. Since the orientation vectors of IRS elements (mirrors) cannot be adjusted perfectly in practice, it is important to evaluate the effects of mismatches between desired and true orientations of IRS elements. To this aim, in the second part of the thesis, we derive the MCRB and the MML estimator for specifying the estimation performance and the lower bound in the presence of mismatches in IRS orientations. We also provide comparisons with the conventional ML estimator and the CRB in absence of orientation mismatches for quantifying the effects of mismatches. It is shown that orientation mismatches can result in significant degradation in localization accuracy at high signal-to-noise ratios.