Abstract
Reconfigurable intelligent surfaces (RISs) are expected to play a significant role in the next generation of wireless cellular technology. This paper proposes an uplink localization scheme using a single-snapshot solution for user equipment (UE) that is located in the near-field of the RIS. We propose utilizing the atomic norm minimization method to achieve super-resolution localization accuracy. We formulate an optimization problem to estimate the UE location parameters (i.e., angles and distances) by minimizing the atomic norm. Then, we propose to exploit strong duality to solve the atomic norm problem using the dual problem and semidefinite programming (SDP). The RIS is controlled and designed using estimated parameters to enhance the beamforming capabilities. Finally, we compare the localization performance of the proposed atomic norm minimization with compressed sensing (CS) in terms of the localization error. The numerical results show a superior performance of the proposed atomic norm method over the CS where a sub-cm level of accuracy can be achieved under some of the system configuration conditions using the proposed atomic norm method.
Recommended Citation
O. Rinchi et al., "Single-Snapshot Localization for Near-Field Ris Model using Atomic Norm Minimization," 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings, pp. 2432 - 2437, Institute of Electrical and Electronics Engineers, Jan 2022.
The definitive version is available at https://doi.org/10.1109/GLOBECOM48099.2022.10000689
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
atomic norm minimization; near-field; Reconfigurable Intelligent Surface (RIS); semidefinite programming (SDP); wireless localization
International Standard Book Number (ISBN)
978-166543540-6
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2023 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2022
Comments
Missouri University of Science and Technology, Grant None