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. 2024 Feb 22;14(1):4350.
doi: 10.1038/s41598-024-54859-3.

RIS-assisted near-field localization using practical phase shift model

Saber Hassouna  1 , Muhammad Ali Jamshed  2 , Masood Ur-Rehman  2 , Muhammad Ali Imran  2 , Qammer H Abbasi  2
Affiliations

RIS-assisted near-field localization using practical phase shift model

Saber Hassouna et al. Sci Rep. .

Abstract

Our research focuses on examining the problem of localizing user equipment (UE) in the uplink scenario using reconfigurable intelligent surfaces (RIS) based lens. We carry out a thorough analysis of the Fisher information matrix (FIM) and assess the influence of various RIS-based lens configurations using an actual RIS phase-dependent amplitude variations model. Furthermore, to reduce the complexity of the maximum likelihood (ML) estimator, a simple localization algorithm-based angular expansion is presented. Simulation results show superior localization performance when prior location information is available for directional and positional channel configurations. The position error bound (PEB) and the root mean square error (RMSE) are studied to evaluate the localization accuracy of the user utilizing the realistic RIS phase-dependent amplitude model in the near-field region. Furthermore, the achievable data rate is obtained in the same region using the realistic RIS phase-dependent amplitude model. It is noticed that adopting the actual RIS phase-dependent amplitude model under the near-field channel increases the localization error and degrades the data rate performance for amplitude value less than one so, the unity assumption of the RIS phase shift model used widely in the literature is inaccurate.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
(a) System setup, (b) coordinate system.
Figure 2
Figure 2
(a) Structure of the RIS including its reflecting element and the equivalent RLC circuit model and (b) amplitude and phase responses for different elements and their corresponding capacitance values.
Figure 3
Figure 3
Reflected amplitude vs. phase shift for RIS element.
Figure 4
Figure 4
Amplitude variations for different values of βmin, γ=0 and ϕ=1.5.
Figure 5
Figure 5
SNR in dB for random, directional and positional phase profile at βmin={0.1,1}.
Algorithm 1
Algorithm 1
User location estimate p(Q^,θ^,φ^) in RIS-assisted near-field localization.
Figure 6
Figure 6
(a) PEB as a function of distance to the RIS lens, βmin∈{0.1,0.3,0.6,1}. (b) PEB as a function of number of RIS elements. (c) PEB versus SNR (dB).
Figure 7
Figure 7
(a) PEB as a function of distance to the RIS lens,σ=1, and βmin∈{0.1,0.3,0.6,1}. (b) PEB as a function of Number of RIS elements σ=1, and βmin∈{0.1,0.3,0.6,1}. (c) PEB versus SNR (dB) σ=1, and βmin∈{0.1,0.3,0.6,1}.
Figure 8
Figure 8
(a) PEB as a function of distance to the RIS lens,σ=0.1, and βmin∈{0.1,0.3,0.6,1}. (b) PEB as a function of Number of RIS elements σ=0.1, and βmin∈{0.1,0.3,0.6,1}. (c) PEB versus SNR (dB) σ=0.1, and βmin∈{0.1,0.3,0.6,1}.
Figure 9
Figure 9
RMSE as a function of distance to the RIS lens, σ=0.1 and βmin∈{0.5,1}.
Figure 10
Figure 10
Data Rate versus RIS distance, βmin∈{0.2,1}.
Figure 11
Figure 11
Data rate versus number of RIS elements, βmin∈{0.2,1}.
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