Reflectarray-Based RIS: Dynamic EM Control

• Reflectarray-based RIS is a programmable metasurface that dynamically controls electromagnetic wavefronts via tunable reflectarray unit cells.
• It achieves real-time beamforming using varactor and PIN-diode tuning, offering high directivity with gains up to 30 dBi in tested prototypes.
• The integration of networked control and advanced signal processing enables adaptive coverage and efficient deployment in future 6G wireless systems.

A reflectarray-based reconfigurable intelligent surface (RIS) is a programmable electromagnetic structure—typically comprising a periodic or quasi-periodic array of planar unit cells—whose primary function is to control electromagnetic wavefronts (phase, amplitude, polarization) via electronically tunable reflection properties. This approach leverages established reflectarray engineering principles, integrating them with emerging metasurface and networked control methodologies to enable real-time and spatially adaptive shaping of wireless propagation environments in, for example, 6G networks.

1. Principles of Reflectarray-Based RIS Operation

Reflectarray-based RISs are composed of a planar (or conformal) array of scattering elements, each capable of imparting a programmable phase shift to locally reflected waves. The operation depends on several core principles:

• Wavefront Control by Local Reflection: Each pixel (unit cell) acts as a passive antenna scatterer, with its reflection phase (and sometimes amplitude and polarization) adjusted by varying electrical parameters—most commonly the capacitance or conductivity—via components such as PIN diodes, varactor diodes, or field-effect transistors.
• Programmable Phase Synthesis: For engineered beamforming, the RIS controller assigns a phase shift $\Phi_n$ to the $n$-th cell such that the sum total of the reflected field forms a desired spatial pattern (e.g., directive beam, multiple beams, sector/omni-illumination).
• Binary and Quantized Phase Control: In many practical reflectarray-based RIS designs, the elements support only binary (0, $\pi$) or moderate-resolution (e.g., 2–4 bit) phase states, with the local phase shift $\Phi_n$ determined by control logic and electronics (Gros et al., 2021, Sayanskiy et al., 2022, Hao et al., 20 Mar 2024).
• Reflectarray vs. Traditional RISs: While all network-controlled metasurfaces alter EM boundary conditions, the reflectarray-based RIS specifically applies the reflectarray paradigm—i.e., passive planar array with spatial phase programming, conventionally used for fixed-beam satellite or radar antennas—to dynamically tunable and possibly large-scale surfaces (Kayraklık et al., 29 Sep 2025).

2. Physical Implementation and Electronic Tuning Architectures

Reflectarray-based RISs have been realized in a variety of physical platforms, mostly using multilayer PCB techniques to accommodate unit-cell electronics, bias networks, and RF interfaces:

• Unit Cell Structure: Each reflectarray unit cell typically consists of a main radiating patch (operating near a predetermined resonance) and one or more mechanisms to control its electrical length or resonance characteristics. Capacitively coupled parasitic patches may be present; varactor or PIN diode loading changes the resonant condition, shifting the phase of the reflected wave (Gros et al., 2021, Sayanskiy et al., 2022, Li et al., 8 May 2025).
• Switching and Control: Binary phase control is often realized with PIN diodes toggled via digital logic, while continuous phase control leverages varactor-tuned capacitance with analog voltage control (Li et al., 8 May 2025). Control signals may be delivered via wired matrixes (e.g., shift registers and FPGA), remotely using infrared digital codes (Sayanskiy et al., 2022), or, in modular architectures, distributed across locally autonomous microcontroller-equipped building blocks.
• Scalability and Modularity: Designs such as the 2D modular reflectarrays allow for scalable aperture construction: identical blocks (e.g., 4 elements each) can be combined up to large surfaces, each independently programmable (Sayanskiy et al., 2022, Kayraklık et al., 29 Sep 2025). Some architectures use wave-controlled biasing lines to reduce the wiring bottleneck in large arrays (Itzhak et al., 3 Sep 2024).
• Polarization and Dual-Band Operation: Advanced unit cells accommodate multiple polarizations and, in some cases, transmission as well as reflection (e.g., dual-functional RIS) (Ma et al., 2022, Ramezani et al., 21 Sep 2024).

3. Electromagnetic Modeling and Analytical Frameworks

The analytical modeling of reflectarray-based RISs draws on both classical array theory and more advanced electromagnetic field synthesis:

• Array Factor and Phase Distribution: The scattered field in the desired direction is synthesized by assigning to each cell a phase shift compensating the propagation delays between source, RIS, and destination. In the near field (reflectarray mode), the phase compensation incorporates spherical-wave propagation:

$$\varphi_{n,m} = -k \sin\theta_{Rx}(x_n \cos\phi_{Rx} + y_m \sin\phi_{Rx}) + k\sqrt{(x_n - x_{Tx})^2 + (y_m - y_{Tx})^2 + z_{Tx}^2}$$

In the far field (access point extender configuration), plane-wave approximations dominate:

$$\varphi_{n, m} = -k \sin\theta_{Rx}(x_n \cos\phi_{Rx} + y_m \sin\phi_{Rx}) + k \sin\theta_{Tx}(x_n \cos\phi_{Tx} + y_m \sin\phi_{Tx})$$

The output field from the RIS is thus

$$E_r(\theta, \phi) = \cos\theta \sum_{n, m} \Gamma_{n,m} E_i(x_n, y_m) \cos\theta_{nm} \exp\big(-j k \sin\theta (x_n \cos\phi + y_m \sin\phi)\big)$$

where $\Gamma_{n,m}$ is the local reflection coefficient, set to $0$ or $\pi$ phase in the binary design (Gros et al., 2021).

• Modeling Approaches: Beyond simple ray tracing, a dipole framework—where each cell is described by a polarizable magnetic dipole—enables accurate prediction of amplitude, phase, and mutual coupling effects, bridging the gap between weakly coupled analytical models and full-wave simulations (Diebold et al., 2022, Hao et al., 20 Mar 2024).
• Quantization Effects: Limitations in phase resolution impose quantization lobes or non-ideal beam shapes; however, even with 1–2 bits of resolution, strong directive beams and effective wavefront control are achievable, with higher resolutions (3–4 bits) yielding nearly ideal behavior (Hao et al., 20 Mar 2024, Gros et al., 2021).

4. Experimental Validation and Performance Metrics

Numerous prototypes have been fabricated and tested to validate both design principles and system-level performance:

• Frequency Bands: Prototypes operate from sub-6 GHz (e.g., Wi-Fi band at 5.2 GHz, C-band at 5–6 GHz, n78 at 3.7–3.8 GHz) up to mmWave (28.5 GHz, 25.8 GHz) and D-band (150 GHz) (Gros et al., 2021, Sayanskiy et al., 2022, Tohidi et al., 2023, Kayraklık et al., 29 Sep 2025).
• Laboratory Beamforming: In controlled anechoic chamber environments, RISs achieve high directivity (close to 30 dBi), with beam steering up to 60°, sidelobe levels around –13 dB, and transmission gains exceeding 15–25 dB compared to metallic plates (Gros et al., 2021, Kayraklık et al., 29 Sep 2025).
• 3D Beamforming and Environmental Adaptation: High-voltage-resolution varactor control enables real-time three-dimensional beamsteering in indoor settings, with experimentally demonstrated 10 dB power gain between targeted locations (Ratajczak et al., 2023).
• Energy Consumption: Passive binary-PIN-diode reflectarrays operate at only the power required for biasing the diodes (e.g., 8 W for a 400-element 10 cm×10 cm array at mmWave) (Gros et al., 2021). Designs featuring varactors consume power in the femto–picoWatt range per unit cell (Sayanskiy et al., 2022, Ratajczak et al., 2023).
• Active RIS Variants: Designs integrating amplifiers (FET-based or with full-duplex relay stages) demonstrably improve coverage, with 12 dB received power gain over passive modes, and with quasi-omnidirectional or sector-shaped beam patterns (Radpour et al., 2023, Ma et al., 2021).
• Hardware Impairments: Practical factors such as specular reflection, beam squint, and mutual coupling impact the effective SNR, with specular reflection potentially reducing SIR by several dB and beam squint imposing frequency-dependent gain losses—especially at D-band (Tohidi et al., 2023).

5. Network Architecture, Control Strategies, and System-Level Integration

Reflectarray-based RISs are increasingly integrated into advanced wireless network topologies:

• Control Architectures: Large RIS panels are organized using master–slave controller hierarchies, distributed IR or wired digital signaling, or even wave-based biasing lines to simplify wiring (Sayanskiy et al., 2022, Kayraklık et al., 29 Sep 2025, Itzhak et al., 3 Sep 2024).
• System-Level Modeling: Realistic deployment scenarios (e.g., in the 3GPP Urban Macro model) confirm that reflectarray-based RIS can yield received power gains of several dB and extend effective area-of-influence for coverage and localization (Gu et al., 2022, Alexandropoulos et al., 2023).
• Codebook-Based Operation: Empirical measurement campaigns provide the basis for codebook selection for RIS patterning. By creating a library of spatially resolved patterns, the RIS can adaptively switch between codewords depending on scenario, with differences of tens of dB observed between patterns under varying spatial, polarization, and coupling conditions (Hatka et al., 27 Mar 2025).
• Dual-Polarization and Broadcasting: RISs with dual-polarized elements enable broad beam/omnidirectional broadcast using Golay complementary array pairs, ensuring a uniform angular coverage for cell-specific signaling (Ramezani et al., 21 Sep 2024).
• Machine Learning for Configuration: Graph neural network (GNN)-based architectures have been introduced for joint optimization of beamforming and reflectarray phase profiles, demonstrating strong scalability and performance in large multi-RIS environments (Lim et al., 24 Jan 2025).

6. Applications, Limitations, and Future Directions

Reflectarray-based RIS technology underpins a broad array of current and anticipated applications:

• Wireless Access and Coverage Extension: RIS deployments address challenging propagation environments, e.g., obstructed mmWave links, non-line-of-sight indoor scenarios, and connectivity enhancement at cell edges (Gros et al., 2021, Kayraklık et al., 29 Sep 2025).
• Energy-Efficient Communications: By controlling the passive EM environment rather than increasing transmit power, RISs offer a pathway to green wireless networks, with minimal active energy consumption at the node (Gros et al., 2021, Sayanskiy et al., 2022).
• Sector and Pattern Engineering: Via subwavelength surface-wave assistance and advanced optimization, RISs can implement sector patterns with controlled beamwidth and amplitude taper, enhancing multiplexing and interference rejection (Ataloglou et al., 8 Apr 2025).
• Implementation Challenges: Key challenges include phase quantization limitations, control wiring complexity (mitigated by wave-controlled and modular approaches), material and device losses (particularly at high frequency), and environmental sensitivity.
• Emerging Research: Future work will focus on reconfigurable and multi-functional unit cell architectures, higher bit-depth phase control, dual-functional (reflection/transmission) RISs, real-world field trials, integration with AI-driven control frameworks, and methodologies for robust operation under hardware and channel nonidealities (Ma et al., 2022, Itzhak et al., 3 Sep 2024).

Reflectarray-based RISs demonstrate both practical feasibility and substantial flexibility for programmable EM environments, validated across a wide range of frequencies, architectures, and use-cases. Through synergy between classical reflectarray engineering, modern metasurface science, and networked control, they are poised to become critical enablers of future wireless systems.