Browsing by Subject "Reconfigurable intelligent surfaces"

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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
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.