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

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.

💡 Research Summary

This paper presents a comprehensive study of a multi‑functional reconfigurable intelligent surface (MF‑RIS) that simultaneously supports radar sensing and 5G New Radio (NR) communication in indoor Sub‑6 GHz environments. The authors first motivate the need for RIS‑based solutions in the context of exploding data traffic and power consumption in 5G/6G networks, highlighting the shortcomings of conventional RIS deployments—high cost, potential interference, and deployment complexity. By integrating radar, energy harvesting, and even solar panels, the MF‑RIS is positioned as a “loose‑integration” platform for integrated sensing and communication (ISAC).

A thorough literature review reveals that most RIS channel measurement campaigns focus on far‑field scenarios, with little attention to near‑field behavior, especially when the RIS is tasked with both sensing and communication functions. To fill this gap, the authors conduct extensive indoor measurements in two distinct environments—a large office space and a medium‑sized conference room—using a custom‑built MF‑RIS that incorporates a millimeter‑wave radar module.

The theoretical foundation begins with a mmWave MIMO radar model. The transmitted signal is pre‑coded (Φ s) and the received signal at each antenna incorporates the target’s range‑dependent phase shift and a complex scattering coefficient κ that depends on the radar cross‑section (RCS). From this model, the Cramér‑Rao lower bound (CRLB) for target localization is derived, providing a benchmark for radar accuracy.

For the communication side, the authors decompose the BS‑RIS‑UE link into three sub‑links: BS‑RIS (typically LoS), BS‑UE (rich multipath), and RIS‑UE (dominantly LoS but also RIS‑induced NLoS components). The virtual LoS (vLoS) component—formed by the concatenation of the BS‑RIS and RIS‑UE LoS paths—is identified as the dominant propagation mechanism in the RIS near‑field. Path loss is modeled using the 3GPP TR 38.901 Close‑In (CI) formulation, augmented with a free‑space loss term that explicitly incorporates the RIS RCS as a function of incidence and reflection angles. The RCS expression (Eq. 11) captures the RIS’s physical aperture, design‑specific loss factor, and sinc‑squared angular dependence, enabling scalable ray‑tracing integration. Empirical results show that the RIS‑UE path loss exponent is less than the free‑space value of 2, confirming the RIS’s “gain‑like” behavior.

Small‑scale fading is addressed with a geometry‑based stochastic model (GBSM) that partitions the indoor environment into Q clusters (moving humans, static walls/furniture, and the static RIS). Each cluster contributes a set of multipath components characterized by mean AoA/AoD, delay, and power. Statistical analysis demonstrates that the conventional Rayleigh model inadequately fits the MF‑RIS‑assisted channel; instead, a Weibull distribution better describes wide‑band fading, while a log‑normal distribution fits narrow‑band scenarios.

Phase synthesis for the RIS is meticulously detailed for both near‑field (Fresnel‑zone) and far‑field (plane‑wave) regimes. Near‑field synthesis requires distance‑dependent phase compensation, whereas far‑field synthesis relies on standard beam‑steering. This dual‑mode design enables the RIS to focus energy on a moving user while preserving the radar waveform.

A real‑time user localization algorithm is proposed, combining density‑based spatial clustering of applications with noise (DBSCAN) to identify radar reflections and a Kalman filter applied per coordinate to predict motion. Experimental evaluation reports an average prediction latency of 127 ms and an angle‑of‑arrival (AoA) estimation error of 6.47°, demonstrating suitability for dynamic tracking.

System‑level validation is performed with a 2 × 2 single‑user MIMO (SU‑MIMO) 5G NR link. When the MF‑RIS is placed within its near‑field, throughput variance is reduced by 74 % and the sum‑rate improves by 12.5 % compared with a baseline without RIS. This performance gain is attributed to the RIS‑induced reduction in RMS delay spread, an increase in coherence bandwidth, and overall channel hardening.

In conclusion, the paper establishes MF‑RIS as a viable hardware platform for concurrent radar sensing and 5G communication, provides a 3GPP‑compatible channel model that captures both large‑scale and small‑scale effects, and validates the concept through extensive indoor measurements and MIMO throughput experiments. Future work is suggested in scaling to multi‑user scenarios, integrating energy‑harvesting functionalities, and incorporating the model into large‑scale network simulators.