Towards Ubiquitous Positioning by Leveraging Reconfigurable Intelligent Surface

Abstract

In future cellular systems, wireless localization and sensing functions will be built-in for specific applications, e.g., navigation, transportation, and healthcare, and to support flexible and seamless connectivity. Driven by this trend, the need rises for fine-resolution sensing solutions and cm-level localization accuracy, while the accuracy of current wireless systems is limited by the quality of the propagation environment. Recently, with the development of new materials, reconfigurable intelligent surfaces (RISs) provide an opportunity to reshape and control the electromagnetic characteristics of the environment, which can be utilized to improve the performance of wireless sensing and localization. In this tutorial, we will first review the background and motivation to utilize wireless signals for sensing and localization. Next, we introduce how to incorporate RIS into applications of sensing and localization, including key challenges and enabling techniques, and then some case studies will be presented. Finally, future research directions will also be discussed.

Reconfigurable Intelligent Surfaces for Localization: Position and Orientation Error Bounds

“…When exploited in receive mode, a large intelligent surface is used to localize a user in front of it, both in near-field and far-field [49], [51]. When instead operating in reflection mode, an approach exploiting the modification of the RIS reflection coefficient is proposed such that the experienced received signal strength (RSS) at different points is enlarged and the localization accuracy is improved [52]. Differently, in [33], authors exploit a RIS for supporting the positioning and communication in the mmwave frequency bands, assuming that the mobile is in farfield with respect to the RIS [33].…”

An Overview of Signal Processing Techniques for RIS/IRS-aided Wireless Systems

“…As far as the localization algorithms, the authors of [221] considered an RIS-assisted 3D localization system, where the AoAs and AoDs were estimated by using the MLE algorithm, and user localization was obtained via a Taylor series algorithm. The localization accuracy of an indoor localization system was improved in [222] and [223] by choosing proper RIS reflection coefficients that maximize the differences of RSS values among adjacent locations. As far as the interplay between communication and localization is concerned, the localization accuracy and the data rate were balanced by optimizing the time allocation of an RISaided wireless communication system in [224].…”

Near-Field Focused Reflectarray Antenna and Reconfigurable Intelligent Surfaces: The Potential of Wave Propagation Control for Smart Radio Environment

“…For the case of an offset-feed reflectarray antenna, the reflectarry aperture plane is illuminated by an offset feed source located at x BS as illustrated in Figure 2(b). The expressions for μ s and μ i are derived based on [25] in the framework of the channel gain formulation, and their product equals μ in Equation (8). These efficiency factors μ s and μ i are mathematically expressed as…”

“…RISs can effectively re-engineer impinging electromagnetic waves in a controlled manner and provide different functions including steering toward any direction, full absorption, polarization manipulation, and more. Therefore, RISs can bring benefits to the wireless communications [4], multiple inputs multiple outputs (MIMO) broadcasting for simultaneous wireless information and power transfer [5], secure transmission [6], backscatter communications [7], and localization and positioning [8].…”