Reconfigurable Intelligent Surfaces for Localization: Position and Orientation Error Bounds (2009.02818v1)

Abstract: Next-generation cellular networks will witness the creation of smart radio environments (SREs), where walls and objects can be coated with reconfigurable intelligent surfaces (RISs) to strengthen the communication and localization coverage by controlling the reflected multipath. In fact, RISs have been recently introduced not only to overcome communication blockages due to obstacles but also for high-precision localization of mobile users in GPS denied environments, e.g., indoors. Towards this vision, this paper presents the localization performance limits for communication scenarios where a single next-generation NodeB base station (gNB), equipped with multiple-antennas, infers the position and the orientation of the user equipment(UE) in a RIS-assisted SRE. We consider a signal model that is valid also for near-field propagation conditions, as the usually adopted far-field assumption does not always hold, especially for large RISs. For the considered scenario, we derive the Cramer-Rao lower bound (CRLB) for assessing the ultimate localization and orientation performance of synchronous and asynchronous signaling schemes. In addition, we propose a closed-form RIS phase profile that well suits joint communication and localization. We perform extensive numerical results to assess the performance of our scheme for various localization scenarios and RIS phase design. Numerical results show that the proposed scheme can achieve remarkable performance, even in asynchronous signaling and that the proposed phase design approaches the numerical optimal phase design that minimizes the CRLB.

• The paper presents a comprehensive localization model using RIS that incorporates a spherical wavefront approach effective in both near-field and far-field conditions.
• It derives the CRLB for synchronous and asynchronous signaling, setting theoretical limits for position and orientation accuracy.
• Simulations confirm that optimized RIS phase profiles significantly reduce localization errors, paving the way for precise positioning in complex environments.

Reconfigurable Intelligent Surfaces for Localization: Position and Orientation Error Bounds

The paper under discussion explores the emerging domain of utilizing Reconfigurable Intelligent Surfaces (RIS) for enhancing localization tasks, specifically concerning mobile user position and orientation estimation. The application of RIS in reconfigurable environments is an advanced concept gaining traction in next-generation communication networks such as 6G, where the environment transitions from passive to an intelligently controlled medium facilitating both communication and localization with unprecedented precision.

Key Contributions

1. Generalized Localization Model: The authors present a comprehensive signal model that does not restrict itself to traditional far-field assumptions. They introduce a spherical wavefront model that remains accurate even in near-field scenarios, which are particularly relevant when dealing with large intelligent surfaces and environments with confined spaces like indoors or urban canyons.
2. CRLB Derivation: The paper rigorously derives the Cramer-Rao Lower Bound (CRLB) for both synchronous and asynchronous signaling within RIS-assisted smart radio environments (SRE). This benchmark provides insights into the theoretical limits of position and orientation estimation accuracy. Notably, the derived CRLB accounts for complications like synchronization errors and geometric layout between the RIS, base station (BS), and mobile station (MS).
3. RIS Phase Profile Optimization: A noteworthy innovation in the paper is the proposed RIS phase shifting strategy, which optimizes the joint task of communication and localization. By maximizing the signal-to-noise ratio (SNR), this methodology approaches the optimal configuration that minimizes CRLB, offering near-optimal performance with feasible complexity.
4. Extensive Simulations: Through simulations, the authors validate their theoretical findings across various RIS configurations and parameter settings. The simulations demonstrate that the localization errors are significantly reduced, achieving high precision even with asynchronous signaling schemes.

Practical and Theoretical Implications

The deployment of RIS in localization enables environments to be viewed as active systems contributing to signal processing and information relay. Such implementations indicate a shift towards embedding intelligence in the physical spaces, thereby increasing the reliability and accuracy of mobile positioning systems, especially in GPS-denied environments. The potential applications span several domains such as augmented reality, autonomous vehicles, and precision mapping.

From a theoretical standpoint, the consideration of both near-field and far-field conditions in RIS-assisted environments broadens our understanding of spatial signal processing limits. The introduction of error bounds such as CRLB in these contexts aids in setting benchmarks for future empirical research and prototype development.

Future Directions

Building on this research, future work could explore multi-RIS environments, investigating cooperative localization strategies and the impact of meta-material properties on localization accuracy. There is also potential for exploring deeper integration with dynamic channel models that account for user mobility and environment variability. Furthermore, real-world implementation challenges, including phase quantization and hardware imperfections, need thorough exploration.

In summary, this paper provides a substantial theoretical framework and practical insights into the utilization of RIS for enhanced localization in next-generation networks. It lays the groundwork for future research aiming to further exploit incident wave control for precise and reliable mobile positioning and orientation in complex environments.