Multi-Functional RIS-enabled Radar and Communication Coexistence: Channel Modeling and a Sub-6 GHz Indoor Measurement Campaign

TL;DR

This work develops a MF-RIS-enabled radar and communication coexistence framework for Sub-6 GHz indoor settings. It introduces a 3GPP-compatible RCS-based channel model, near-field and far-field phase-synthesis codebooks, and a DBSCAN+Kalman localization pipeline, validated by indoor measurements and a 5G NR SU-MIMO throughput test. The results reveal Weibull or log-normal fast fading instead of Rayleigh, substantial channel hardening in the RIS near-field, and a 12.5% sum-rate improvement with a 74% reduction in throughput variance when the MF-RIS is active. Overall, the study demonstrates practical MF-RIS gains for ISAC-like RCC systems, enabling robust, low-delay wireless performance in indoor environments.

Abstract

In this work, we analyze a multi-functional reconfigurable intelligent surface (MF-RIS)-enabled radar and communication coexistence (RCC) system, detailing the key aspects of its phase synthesis codebook generation and the implemented localization algorithm for real-time user tracking based on density-based spatial clustering of applications with noise (DBSCAN), which features a Kalman filter for the prediction of user mobility. We derived a 3GPP-compatible radar cross-section (RCS) and re-radiation pattern-based channel model for the described MF-RIS system, supplementing it with channel measurements. We obtained large and small-scale characteristics, including path loss, shadow fading, Rician K-factor, cluster powers, and RMS delay spread. The study finds that Sub-6 GHz indoor propagation is largely free of blind spots, even with a blocked line-of-sight (LoS) path. Therefore, the proposed channel model includes non-line-of-sight (NLoS) paths, including the ones created by the MF-RIS. We also performed an experimental evaluation of the channel throughput in a fifth generation (5G) new radio (NR) single user multiple-input-multiple-output (SU-MIMO) system, reporting a 74\% reduction in throughput variance and a 12.5\% sum-rate improvement within the MF-RIS near-field compared to the no-RIS setup. This result shows that the MF-RIS can minimize delay spread and increase the coherence bandwidth by creating virtual-LoS (vLoS) path for the moving user, thereby effectively hardening wireless MIMO channels.

Figures (11)

• Figure 1: Overview of the GBSM applied to model the fast-fading channel ($h_{\text{BS-RIS-UE}}$) in this work.
• Figure 2: Measured $h_{\text{BS-RIS-UE}}$ path loss exponent(s) based on the CI and the CIF model fits with variable $d_{\text{RIS-UE}}$.
• Figure 3: Channel histograms for the UE coordinate (40$^\circ$, 90$^\circ$, 2m) plotted on measured PDP with: a) RIS switched OFF, showing fading in the BS-UE NLoS channel, and b) RIS switched ON, showing fading in the cascaded BS-RIS-UE channel i.e. the vLoS.
• Figure 4: Horizontal AoA Tracking: CRLB vs predicted AoA vs measured AoA from a UE moving in the radar's FoV, and the resulting AoA error.
• Figure 5: MF-RIS phase synthesis algorithm for near-field and far-field domains.