Bi-Static Sensing for Near-Field RIS Localization

Abstract

We address the localization of a reconfigurable intelligent surface (RIS) for a single-input single-output multicarrier system using bi-static sensing between a fixed transmitter and a fixed receiver. Due to the deployment of RISs with a large dimension, near-field (NF) scenarios are likely to occur, especially for indoor applications, and are the focus of this work. We first derive the Cramér-Rao bounds (CRBs) on the estimation error of the RIS position and orientation and the time of arrival (TOA) for the path transmitter-RIS-receiver. We propose a multi-stage low-complexity estimator for RIS localization purposes. In this proposed estimator, we first perform a line search to estimate the TOA. Then, we use the far-field approximation of the NF signal model to implicitly estimate the angle of arrival and the angle of departure at the RIS center. Finally, the RIS position and orientation estimate are refined via a quasi-Newton method. Simulation results reveal that the proposed estimator can attain the CRBs. We also investigate the effects of several influential factors on the accuracy of the proposed estimator like the RIS size, transmitted power, system bandwidth, and RIS position and orientation.

Joint User Localization and Location Calibration of a Hybrid Reconfigurable Intelligent Surface

“…We can solve the problem (24) through 1D line search or gradient-based iterative search with an initial point, which can be provided by an FFT-based method [26].…”

“…In the second scenario, a UE can use an RIS(s) to enable its localization, where the RIS(s)'s state is unknown and needs to be estimated. This problem is known as RIS localization, which one can see as a bi-static sensing or a calibration problem [26].…”

“…The RIS position or orientation may be unknown for a number of reasons, including poor calibration, lack of knowledge of the environment map in which the RIS is placed, or due to mobility of the RIS itself [39], [40]. The relevant works are only [26], [28] that focused on a passive RIS. In these works, the locations of the transmitters and receivers are assumed to be known, which requires some overhead for infrastructure calibration.…”

Performance of RIS-Aided Nearfield Localization under Beams Approximation from Real Hardware Characterization

“…Focusing on the specific case of NF localization, many other RIS optimization schemes have been put forward, such as SNR maximization in [25] and [26] and DFT matrices in [27]. On the other hand, a traditional randomized RIS phase design was considered in [28] to achieve bi-static localization of the RIS. In [15], random profiles are also utilized and the authors showed, theoretically, that this profile design can ensure localization continuity in NLoS conditions through direct positioning out of one single RIS-reflected path, relying on NF properties.…”

Joint Power Allocation and Phase-Shift Design for RIS-Aided Cooperative Near-Field Localization

“…Introducing the RIS to assist in communication-based positioning has become a research focus [32], [31]. From the perspective of mobility, the research can be divided into two categories: static positioning [17], [18], [19] and dynamic positioning [15], [13]. The former considers scenarios where the user undergoes minimal movement within a coherent time, and the latter involves scenarios with the opposite characteristic.…”