Reconfigurable Intelligent Surface-Aided Near-field Communications for 6G: Opportunities and Challenges

TL;DR

This work argues that RIS-aided near-field communications are essential for 6G, driven by spherical-wave propagation and a Rayleigh-distance boundary that reveals higher DoFs and beamfocusing capabilities. It develops two RIS classes—patch-array and metasurface—alongside corresponding near-field channel models, and analyzes fundamental limits via power scaling laws and effective DoFs, highlighting the superior near-field performance of metasurface RISs. To tackle CSI and computational challenges, the paper proposes a two-stage hierarchical near-field beam training framework with polar-domain codebooks and a low-complexity element-wise beamforming method for STAR-RIS, achieving substantial training efficiency and scalable design. The findings illuminate the benefits of near-field RISs for high-rate, multi-user 6G links and point to open research avenues in metasurface modeling, dynamic aperture control, and AI-assisted optimization.

Abstract

Reconfigurable intelligent surface (RIS)-aided near-field communications is investigated. First, the necessity of investigating RIS-aided near-field communications and the advantages brought about by the unique spherical-wave-based near-field propagation are discussed. Then, the family of patch-array-based RISs and metasurface-based RISs are introduced along with their respective near-field channel models. A pair of fundamental performance limits of RIS-aided near-field communications, namely their power scaling law and effective degrees-of-freedom, are analyzed for both patch-array-based and metasurface-based RISs, which reveals the potential performance gains that can be achieved. Furthermore, the associated near-field beam training and beamforming design issues are studied, where a two-stage hierarchical beam training approach and a low-complexity element-wise beamforming design are proposed for RIS-aided near-field communications. Finally, a suite of open research problems is highlighted for motivating future research.

Figures (7)

• Figure 1: Illustration of RIS-aided far-field and near-field wireless communications, where the RIS can refract and/or reflect the incident signal to surrounding users.
• Figure 2: Illustration of patch-array-based RISs and metasurface-based RISs.
• Figure 3: Three stages of the signal being refracted/reflected by the metasurface-based RIS to the receiver.
• Figure 4: Power scaling laws for patch-array-based and metasurface-based RISs. The receiver is located at a fixed distance of $d=10$ meters with respect to the centre of RIS and the dimension of the receiver is $z_R \approx 5.0$ cm. The number of patch-array-based RIS elements ranges from $1$ to $20$ and the aperture size of the metasurface-based RIS ranges from $1$ cm$^2$ to $4$ cm$^2$. The wavelength of the carrier signal is $0.01$ m.
• Figure 5: Illustration of the beam training in RIS-aided near-field communications.