⚡ electrical engineering

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

This paper presents a comprehensive analysis of a multi-functional reconfigurable intelligent surface (MF-RIS) system for radar and communication coexistence, combining a 3GPP-compatible channel model with Sub-6 GHz indoor measurements to demonstrate how MF-RIS creates virtual line-of-sight paths that eliminate blind spots, reduce throughput variance, and improve sum-rate in 5G SU-MIMO environments.

Original authors: Anton Tishchenko, Demos Serghiou, Hamidreza Taghvaee, Arman Shojaeifard, Ahmed Elzanaty, Gabriele Gradoni, Mohsen Khalily, Rahim Tafazolli

Published 2026-02-09

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CC BY 4.0

Original authors: Anton Tishchenko, Demos Serghiou, Hamidreza Taghvaee, Arman Shojaeifard, Ahmed Elzanaty, Gabriele Gradoni, Mohsen Khalily, Rahim Tafazolli

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are trying to have a conversation in a crowded, echoey room where the person you are talking to is walking around. Sometimes the direct line of sight is blocked by a wall, or the sound bounces off so many different surfaces that your voice gets jumbled and weak. This is exactly the problem modern wireless networks (like 5G) face indoors: signals get lost, slow down, or fluctuate wildly when users move.

This paper introduces a clever solution called a Multi-Functional Reconfigurable Intelligent Surface (MF-RIS). Think of this not as a standard antenna, but as a "smart mirror" for radio waves.

Here is a breakdown of what the researchers did and found, using simple analogies:

1. The Problem: The "Echo Chamber" Effect

In a typical indoor room, your phone's signal hits walls, furniture, and people, bouncing around like a pinball. This creates a messy environment where the signal strength jumps up and down unpredictably. If you are moving, the connection can drop or slow down because the "pinball" paths are constantly changing.

2. The Solution: The "Smart Mirror" (MF-RIS)

The researchers built a large, flat panel (the MF-RIS) that acts like a smart mirror.

It doesn't just reflect; it focuses. Unlike a regular mirror that reflects light in a fixed direction, this smart mirror can be programmed to bend radio waves precisely toward a specific person, even if they are moving.
It creates a "Virtual Line of Sight." Even if a wall blocks the direct path between the cell tower (Base Station) and your phone, the mirror creates a new, clean path. It's like the mirror is building a temporary, invisible bridge for the signal to cross the room without hitting obstacles.

3. The "Eyes" of the System (Radar Integration)

This isn't just a passive mirror; it has "eyes." The researchers integrated a radar system into the setup.

The Tracker: Imagine the mirror has a built-in radar that constantly scans the room to see exactly where you are walking.
The Reflex: As you move, the radar tells the mirror, "The person moved left!" The mirror instantly adjusts its surface (like thousands of tiny, programmable tiles) to keep the signal focused on you. This happens in real-time, with a slight delay of about 0.1 seconds (roughly the time it takes to blink).

4. The Experiment: Testing the Mirror

The team set up two different indoor scenarios: a large auditorium and a smaller meeting room. They blocked the direct signal path to simulate a difficult environment and then turned the "smart mirror" on and off to compare results.

What they measured:

The "Blur" (Delay Spread): In a messy room, signals arrive at different times (some bounced off the ceiling, some off the floor), causing a "blur" that slows down data. The mirror reduced this blur significantly by creating a single, strong, direct path.
The "Stability" (Channel Hardening): Without the mirror, the signal strength was like a flickering lightbulb. With the mirror, it became a steady, bright beam.
The "Speed" (Throughput): They tested actual internet speeds on a 5G phone.

5. The Results: What Happened?

When they turned the MF-RIS on, the results were impressive:

Smoother Connection: The variation in speed dropped by 74%. This means the connection stopped stuttering and became very stable, even while the user was walking.
Faster Speeds: The total data speed improved by 12.5%.
No "Blind Spots": They found that even when the direct path was blocked, the mirror created a strong alternative path, effectively eliminating "dead zones" in the room.

6. The "Math" Behind the Magic

The researchers didn't just guess; they built a new mathematical model to describe how these mirrors work.

They found that old models (which assumed signals behave like random static) didn't fit. Instead, the signals with the mirror behaved more predictably, like a focused beam.
They figured out that the mirror needs to be programmed differently depending on how far away you are. If you are close (near-field), the mirror has to focus like a magnifying glass. If you are far away (far-field), it acts more like a standard reflector. They created specific instructions (codebooks) for both scenarios.

Summary

In short, this paper proves that a smart, radar-equipped mirror can fix the messy, unpredictable nature of indoor 5G signals. By tracking a moving user and constantly adjusting the reflection, it creates a stable, high-speed "virtual tunnel" for data, making the wireless connection feel as solid and reliable as a wired cable, even in a crowded, obstacle-filled room.

Problem Statement

The rapid evolution of 5G and the projected growth of private networks have placed unprecedented demands on network quality and power consumption. While Reconfigurable Intelligent Surfaces (RIS) offer a low-power solution to enhance wireless propagation, their practical deployment is hindered by high costs, potential interference, and deployment complexities. Furthermore, the literature lacks detailed studies on Multi-Functional RIS (MF-RIS) systems that integrate sensing (radar) and communication, particularly regarding:

Channel Modeling: There is a scarcity of accurate fast-fading channel models for RIS-assisted links that account for non-line-of-sight (NLoS) components and near-field effects, which dominate indoor scenarios.
Localization: Practical measurement-based studies on user equipment (UE) localization using integrated radar on RIS are largely unexplored.
Near-Field Dynamics: The behavior of RIS in the near-field (where the surface size is large relative to the wavelength) remains largely uncharacterized compared to far-field assumptions.

Methodology

The authors conducted a comprehensive indoor measurement campaign at the University of Surrey's 5G/6G Innovation Centre using a custom MF-RIS system.

1. System Architecture:

MF-RIS Hardware: A 4-bit digital RIS operating at 3.5 GHz (Sub-6 GHz) with 1,850 unit cells ( $50 \times 37$ ). It integrates a 60 GHz mmWave radar (TI IWR6843ISK) for user tracking.
Communication Setup: A 2x2 5G New Radio (NR) Single User MIMO (SU-MIMO) system using LimeNET hardware and a 4x4 MIMO smartphone for throughput testing.
Scenarios: Measurements were performed in two indoor environments: a large auditorium ( $14.6 \times 10.8$ m) and a meeting room ( $7.3 \times 4.3$ m). Both scenarios featured a blocked Line-of-Sight (LoS) between the Base Station (BS) and UE, relying on NLoS paths and the RIS.

2. Theoretical Framework:

Channel Modeling: The authors derived a 3GPP-compatible Geometric-Based Stochastic Model (GBSM) for the BS-RIS-UE channel. This model incorporates:
- Radar Cross-Section (RCS) and re-radiation patterns for the RIS.
- A cascaded channel structure including the BS-RIS LoS, RIS-UE LoS (creating a "Virtual LoS" or vLoS), and the direct BS-UE NLoS path.
- Fast-fading statistics derived from empirical data, testing distributions beyond the conventional Rayleigh model.
Localization Algorithm: A real-time UE tracking algorithm was implemented using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) on radar point clouds, followed by a Kalman filter for mobility prediction and phase synthesis updates.
Phase Synthesis: Distinct codebook generation methods were derived for:
- Near-Field: Using spherical wavefront models and Green's functions to compensate for propagation phase and Fresnel curvature.
- Far-Field: Using linear phase gradients for beam steering.

3. Experimental Procedure:

Static Measurements: Used a waveform generator and real-time oscilloscope to capture complex channel responses, Power Delay Profiles (PDP), and delay spreads at various angles (5°–60°) and distances (1–10 m).
Dynamic Measurements: A moving UE (constant velocity) was tracked to evaluate system throughput and channel hardening effects in real-time.

Key Contributions

3GPP-Compatible Channel Model: The paper presents a derived channel model based on RIS RCS and re-radiation patterns, compatible with 3GPP TR 38.901. It explicitly models the vLoS component and the NLoS BS-UE path, which are often neglected in existing literature.
Fading Distribution Analysis: The study demonstrates that the conventional Rayleigh fading model is unsuitable for MF-RIS-assisted channels. Instead, the channel is better described by a Weibull distribution over wide bandwidths and a log-normal distribution over narrow bandwidths.
Near-Field Codebook Derivation: The authors provide exact derivations for near-field phase synthesis, highlighting the fundamental differences from far-field linear phase gradients required to focus energy in the near-field region.
Integrated Localization and Tracking: A practical localization algorithm is detailed, achieving an average prediction latency of 127 ms and an average Angle of Arrival (AoA) estimation accuracy of 6.47°.
Empirical Channel Characterization: Comprehensive large-scale (path loss, shadow fading) and small-scale (Ricean K-factor, RMS delay spread, cluster powers) parameters were measured and tabulated for both RIS-on and RIS-off scenarios.

Results

Channel Hardening: The MF-RIS effectively creates a vLoS path, "hardening" the wireless MIMO channel. This reduces the randomness of the channel gain.
Delay Spread Reduction: In the near-field (2 m), the RIS significantly reduced the RMS delay spread compared to the NLoS-only scenario, thereby increasing the coherence bandwidth.
Throughput Performance:
- In the 2x2 5G NR SU-MIMO system, the addition of the MF-RIS resulted in a 74% reduction in throughput variance and a 12.5% improvement in sum-rate within the near-field compared to the no-RIS setup.
- At a 4 m distance, the system still showed improvements (8.2% average throughput increase), though gains were smaller as the channel moved toward the far-field.
Blind Spot Mitigation: The study found that Sub-6 GHz indoor propagation with MF-RIS is largely free of blind spots, even when the direct LoS is blocked, due to the creation of reliable vLoS paths.
Localization Accuracy: The radar-based tracking maintained alignment with the Cramér–Rao Lower Bound (CRLB) for static or constant-velocity users, with errors aligning quickly (3–4 frame durations) after trajectory changes.

Significance and Claims

The paper claims that the proposed MF-RIS system addresses critical gaps in the deployment of RIS technology by demonstrating a practical, integrated sensing and communication platform. Its significance lies in:

Validating the "Loose Integration" of ISAC: It shows that MF-RIS can enable Integrated Sensing and Communication (ISAC) with minimal hardware changes to existing 5G architectures.
Providing a Measurement-Driven Model: By supplementing theoretical derivations with extensive empirical data, the paper offers a validated channel model that can be used in ray-tracing simulators and network planning tools.
Demonstrating Channel Hardening: The results provide evidence that MF-RIS can stabilize MIMO channels in dynamic indoor environments, reducing throughput fluctuations and improving reliability for moving users.
Near-Field Feasibility: The work confirms that near-field focusing is achievable and beneficial for indoor scenarios, challenging the assumption that RIS is only effective in far-field conditions.

The authors conclude that while the MF-RIS introduces complexity in codebook design and phase synthesis, the resulting improvements in channel stability, throughput, and localization accuracy make it a viable candidate for future 6G indoor networks.