[Paper Review] Hybrid Reconfigurable Intelligent Metasurfaces: Enabling Simultaneous Tunable Reflections and Sensing for 6G Wireless Communications

George C. Alexandropoulos, Nir Shlezinger+4 more|arXiv (Cornell University)|Apr 10, 2021

Advanced Wireless Communication Technologies60 citations

TL;DR

This paper introduces Hybrid Reflecting and Sensing RISs (HRISs) that can simultaneously reflect and sense impinging signals, enabling self-configuration, AoA estimation, and improved channel estimation for 6G; it provides hardware design, a simple HRIS model, and proof-of-concept simulations.

ABSTRACT

The latest discussions on the upcoming sixth Generation (6G) of wireless communications are envisioning future networks as a unified communications, sensing, and computing platform. The recently conceived concept of the smart radio environment, enabled by Reconfigurable Intelligent Surfaces (RISs), contributes towards this vision offering programmable propagation of information-bearing signals. Typical RIS implementations include metasurfaces with almost passive unit elements capable of reflecting their incident waves in controllable ways. However, this solely reflective operation induces significant challenges for the RIS optimization from the wireless network orchestrator. For example, RISs lack information to locally tune their reflection pattern, which can only be acquired by other network entities, and then shared with the RIS controller. Furthermore, channel estimation, which is essential for coherent RIS-empowered communications, is challenging with the available RIS designs. This article reviews the emerging concept of Hybrid reflecting and sensing RISs (HRISs), which enables metasurfaces to reflect the impinging signal in a controllable manner, while simultaneously sensing a portion of it. The sensing capability of HRISs facilitates various network management functionalities, including channel parameter estimation and localization, while giving rise to potentially computationally autonomous and self-configuring metasurfaces. We discuss a hardware design for HRISs and detail a full-wave electromagnetic proof of concept. The distinctive properties of HRISs, in comparison to their solely reflective counterparts, are highlighted and a simulation study evaluating their capability for performing full and parametric channel estimation is presented. Future research challenges and opportunities arising from the HRIS concept are also included.

Motivation & Objective

• Motivate the need for metasurfaces that can both reflect and sense to address limitations of purely reflective RISs, such as lack of local sensing and challenging channel estimation.
• Propose a hybrid meta-atom design with sensing capabilities and a waveguide-based sampling mechanism.
• Demonstrate hardware feasibility via full-wave EM simulations and illustrate benefits for AoA estimation and end-to-end channel estimation.
• Highlight research challenges and opportunities toward self-configuring, sensing-enabled RIS-enabled wireless networks for 6G.

Proposed method

• Describe hybrid meta-atom configurations that reflect portions of the incident signal while sensing other portions.
• Couple metasurfaces to sampling waveguides whose outputs feed reception RF chains for local processing.
• Use a simple HRIS operation model with a coupling parameter rho_n to represent the reflected versus sensed energy split.
• Perform full-wave EM simulations at 19 GHz to show simultaneous reflection and sensing with tunable phase shifts.
• Provide a model for HRIS-enabled wireless systems and evaluate AoA estimation and end-to-end channel estimation performance.
• Compare HRIS sensing-enabled estimation capabilities against conventional purely reflective RIS approaches.

Experimental results

Research questions

• RQ1 Can HRISs simultaneously reflect and sense to enable autonomous or semi-autonomous metasurface operation?
• RQ2 How does the sensing capability affect AoA estimation accuracy and channel estimation performance in HRIS-assisted systems?
• RQ3 What is the impact of the energy-splitting parameter rho on reflection quality and sensing accuracy?
• RQ4 How many pilots and RF chains are required for reliable estimation of UT–RIS and RIS–BS channels using HRISs?
• RQ5 What are the practical design considerations and trade-offs for implementing HRIS hardware across frequencies relevant to 6G?

Key findings

• AoA can be accurately estimated with HRISs even when only a small portion (as low as 20%) of the signal is sensed.
• An HRIS with 64 meta-atoms and 8 RF chains can recover UT–RIS and RIS–BS channels from 64 pilots, outperforming some conventional RIS-based schemes that require more pilots.
• Increasing the coupling to the sensing path (higher rho) improves AoA estimation but reduces reflected signal strength, revealing a controllable trade-off.
• Optimizing the coupling parameters rho (including potential per-element optimization) improves cascaded channel estimation performance compared to fixed settings.
• HRIS sensing enables local parameter estimation (AoA, RF sensing, RF mapping) that can facilitate self-configuration and reduce network control overhead, while maintaining RIS-like reflection benefits.
• Compared to purely reflective RISs, HRISs offer potential gains in sensing-enabled channel estimation, with trade-offs in power consumption and hardware complexity.