TY - GEN
T1 - Performance Analysis of RIS-Aided Localization in Wireless Networks Using Stochastic Geometry
AU - Shaikh, Mohammed Aasim
AU - Kouzayha, Nour
AU - Elzanaty, Ahmed
AU - Kishk, Mustafa
AU - Al-Naffouri, Tareq Y.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This study presents a framework to analyze the performance of uplink localization with reconfigurable intelligent surfaces (RISs) in large-scale cellular networks. First, we propose a novel RIS-aided uplink localization algorithm, where the received signal strength (RSS) is observed at the base station (BS) for various pre-defined phase shift patterns of the RIS, i.e., a codebook of beams. We present a maximum likelihood estimator (MLE) and evaluate its performance by comparing it to the position error bound (PEB), defined as the square root of the Cramér-Rae lower bound (CRLB). Then, to analyze the localization performance on a large scale, we employ stochastic geometry tools, allowing the derivation of a tractable expression for the marginal PEB distribution. The obtained results demon-strate that the proposed algorithm converges to the CRLB for a narrow search grid, in a high SNR regime. Furthermore, higher BS density, number of RIS elements, and RIS element size are shown to enhance localization precision.
AB - This study presents a framework to analyze the performance of uplink localization with reconfigurable intelligent surfaces (RISs) in large-scale cellular networks. First, we propose a novel RIS-aided uplink localization algorithm, where the received signal strength (RSS) is observed at the base station (BS) for various pre-defined phase shift patterns of the RIS, i.e., a codebook of beams. We present a maximum likelihood estimator (MLE) and evaluate its performance by comparing it to the position error bound (PEB), defined as the square root of the Cramér-Rae lower bound (CRLB). Then, to analyze the localization performance on a large scale, we employ stochastic geometry tools, allowing the derivation of a tractable expression for the marginal PEB distribution. The obtained results demon-strate that the proposed algorithm converges to the CRLB for a narrow search grid, in a high SNR regime. Furthermore, higher BS density, number of RIS elements, and RIS element size are shown to enhance localization precision.
KW - Cramér-Rao lower bound
KW - localization
KW - received signal strength
KW - reconfigurable intelligent surfaces
KW - stochastic geometry
UR - http://www.scopus.com/inward/record.url?scp=85198832522&partnerID=8YFLogxK
U2 - 10.1109/WCNC57260.2024.10571290
DO - 10.1109/WCNC57260.2024.10571290
M3 - Conference contribution
AN - SCOPUS:85198832522
T3 - IEEE Wireless Communications and Networking Conference, WCNC
BT - 2024 IEEE Wireless Communications and Networking Conference, WCNC 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th IEEE Wireless Communications and Networking Conference, WCNC 2024
Y2 - 21 April 2024 through 24 April 2024
ER -