---
title: Multi-Functional Reconfigurable Intelligent Surface
url: https://www.emergentmind.com/topics/multi-functional-reconfigurable-intelligent-surface-mf-ris
type: topic
---

# Multi-Functional Reconfigurable Intelligent Surface

A Multi-Functional Reconfigurable Intelligent Surface (MF-RIS) is a metamaterial-based panel whose elements simultaneously support wide-aperture signal reflection, transmission (refraction), active amplification, and energy harvesting. In contrast to conventional single-functional RIS (SF-RIS), which provides only passive reflection, MF-RIS introduces additional degrees of freedom via concurrent active and passive EM-wave manipulation, element-wise power splitting between reflection/refraction, and RF-to-DC energy conversion. These capabilities enable MF-RIS to overcome the double-fading attenuation and half-space coverage limitations of SF-RIS, delivering substantial performance improvements in spectral efficiency, network coverage, physical layer security, SWIPT, ISAC, and computation-centric wireless architectures [2301.13630][2405.16257][2310.02564].

## 1. Architecture and Physical Layer Model

Each MF-RIS element consists of three highly integrated layers: (i) a reflective layer with a tunable impedance network (e.g., varactors, PIN diodes) for amplitude and phase control in reflection; (ii) an amplification layer including a low-noise amplifier (LNA), power divider, and DC biasing circuits; and (iii) a refractive (transmission) layer, symmetrically mirroring the reflective side. Key element design parameters are the amplitude-gain coefficients ($\beta_m^r$, $\beta_m^t$) and phase shifts ($\theta_m^r$, $\theta_m^t$) for reflection/transmission at each element $m$, subject to local constraints: $\beta_m^r + \beta_m^t \leq \beta_{\max}$, $0 \leq \beta_m^p \leq \beta_{\max}$, $p\in\{r,t\}$, with $\beta_{\max}\ge 1$ to allow amplification [2301.13630][2405.16257].

The per-element block diagram synthesizes:
- **Signal splitting**, with energy/power divider routing the LNA-amplified signal into reflection and refraction branches.
- **Active gain** control via LNA settings, and **independent phase shift** via bias-tunable impedance networks.
- **Energy harvesting** (in some variants) by integrating RF rectifier circuits that allow switching between harvesting, reflecting, or mixed modes at the element level [2310.02564][2310.02562][2506.01051].

The outgoing MF-RIS reflection and transmission beamforming vectors are:
\[
u_r = [\sqrt{\beta_1^r} e^{j\theta_1^r}, \ldots, \sqrt{\beta_M^r} e^{j\theta_M^r}]^T; \quad
u_t = [\sqrt{\beta_1^t} e^{j\theta_1^t}, \ldots, \sqrt{\beta_M^t} e^{j\theta_M^t}]^T
\]
yielding two independent full-space beamformers, with direct amplitude and phase control per spatial region.

For analysis, the cascaded end-to-end channel for user $k$ (in reflection or transmission half-space) is given by:
\[
\hat{h}_k^H = h_k^H + g_k^H \Theta_k H
\]
where $h_k$, $g_k$, $H$ denote the BS$\to$user-$k$, RIS$\to$user-$k$, and BS$\to$RIS channels. $\Theta_k$ is the diagonal matrix encoding element-wise gains and phases for the corresponding space [2301.13630][2405.16257].

## 2. Joint Optimization Frameworks

MF-RIS brings fundamentally new optimization dimensions for network control:
- **Amplitude and phase design**: Each element’s amplitude split and phase shift must be jointly designed for the desired reflection/refraction/scattering characteristics under power, amplifier, and hardware constraints.
- **Active beamforming**: The BS’s precoder must be tuned in tandem with MF-RIS coefficients, exploiting the new DoFs to maximize performance metrics (sum-rate, secrecy, or energy efficiency).
- **Self-sustainability**: In designs that incorporate energy harvesting, element mode-indicators ($\alpha_m$ for harvesting/active mode) become additional hybrid discrete-continuous control variables [2310.02562][2310.02564][2501.11079][2506.01051].

In NOMA-based downlink, the optimization seeks to maximize the sum-rate over beamformers $\{w_k\}$ and MF-RIS coefficients $\{\Theta_k\}$, subject to BS and RIS power constraints, amplitude/phase limits, per-user QoS, and, if present, energy causality at the RIS [2301.13630][2310.02562]. The core alternating optimization approach decomposes the problem into two subproblems:
- **Beamforming update** (fixed MF-RIS): SDPs via variable lifting (e.g., $W_k=w_k w_k^H$), employing SCA or penalty-based rank-one relaxation.
- **MF-RIS coefficient update** (fixed beamformer): SDPs over amplitude/phase variables, penalizing non-rank-one solutions, and enforcing energy or hardware constraints [2301.13630][2310.02562].

For systems with imperfect CSI, robust beamforming leverages S-procedure LMIs (bounded errors) or Bernstein-type convex reformulations (statistical errors), enforcing worst-case or probabilistic QoS [2412.08032][2310.02564].

In communication-sensing union (ISAC), the objective is multi-objective: maximize communication sum-rate while also maximizing aggregate target sensing SINR. This yields three-block AO—over transmit beamforming, RIS coefficients, and sensing filters—typically solved by sequential convex/quadratic programming, eigen-decomposition, and SCA [2412.01251][2410.06584].

## 3. Performance Analysis and Numerical Insights

MF-RIS consistently outperforms passive RIS, STAR-RIS (dual-functional but passive), and classical active RIS across all major metrics:
- **Spectral efficiency**: In NOMA networks (N=16, M=100, K=6), MF-RIS achieves ~59% sum-rate gain over passive RIS, 16% over active-only RIS, and 44% over STAR-RIS at 10 dBm BS power [2301.13630].
- **Energy efficiency**: MF-RIS attains 24–90% higher EE than dual-functional/passive RIS for reasonable CSI error bounds (N=6, M=32, K=6). Over-provisioning $M$ can degrade EE due to amplified overhead [2412.08032].
- **Physical layer security**: In NOMA, MF-RIS improves secrecy outage/diversity order for external eavesdroppers, with full-space active beamforming and jamming capability exceeding both active RIS and STAR-RIS. For internal Eves, diversity benefits collapse, underscoring deployment and power allocation subtleties [2405.10514].
- **SWIPT**: By optimal partitioning into energy-harvesting, reflection, and transmission sub-surfaces and tuning DCSK waveform/correlation parameters, MF-RIS achieves Pareto-optimal harvested energy vs. BER frontiers unavailable to passive RIS [2506.01051].
- **ISAC performance**: For multi-user multi-target ISAC, MF-RIS with energy splitting (ES) achieves 50–75% higher sensing SINR than active/passive/STAR-RIS at fixed power [2412.01251].
- **Scalability**: Performance scales favourably with $M$ until amplifier power budget or CSI errors dominate; in multi-RIS deployments, DRL-based control of positions and coefficient settings is required for near-optimal system energy efficiency [2601.00538][2501.11079].

Key deployment findings:
- **RIS placement**: For most MF-RIS variants, optimal location is closer to the transmitter to maximize available incident RF for amplification/harvest, in contrast to the passive-RIS optimum (at BS or users) [2310.02562][2310.02564].
- **Self-sustainability**: There exists an optimal fraction of active/harvesting elements; too many active elements starve the RIS of power, while too many harvesting elements limit spatial DoFs [2310.02564][2310.02562][2506.01051].
- **Thermal noise**: Amplifier noise per element sets a cap on feasible amplification gains; at high SNR or large $M$, residual amplifier noise and imperfect SIC dominate performance degradation [2501.10657][2405.10514].

## 4. Advanced MF-RIS Prototypes and Realization

Recent hardware realizations validate the multi-functional paradigm:
- **Thin-film, microcontroller-driven MF-RIS**: A 1×4 CPW-based thin-film MF-RIS on PET achieves independent per-element per-harmonic reflection/refractive phase control using inkjet-printed silver NP circuits, PIN diodes, and MCU control. Simultaneous front/rear beam steering and harmonic multiplexing are demonstrated with 5–6 dBi array gain and <15 dB conversion losses [2406.06343].
- **Partitioned surfaces**: EH/relay clusters, with dynamic OOK control logic and biasing, realize practical time/frequency or mode switching among MF-RIS functions in real-time [2506.01051][2406.06343].
- **Amplification hardware**: Integration of LNA, power divider, and dual-feed impedance networks per element, with low-noise bias/amp circuits and controller modules for adaptive phase/amplitude, is required for true simultaneous multi-functionality [2405.16257][2310.02564].

Fabrication leverages additive manufacturing and off-the-shelf MCU/SMD components for significant cost reduction ($\sim$ $10–15 per 4-element array) and rapid prototyping. Remaining engineering challenges include element coupling, real-time adaptive control, thermal management, and DC power delivery [2406.06343][2405.16257].

## 5. Methodologies for Adaptive and Intelligent Configuration

Given the large-dimensional, hybrid discrete-continuous MF-RIS control space, algorithmic advances are crucial:
- **Alternating optimization** (AO): Decomposition into convex subproblems for beamforming, MF-RIS configuration, and (when present) energy harvesting/control, using semidefinite programming and penalty methods to enforce rank constraints [2301.13630][2310.02562][2412.08032].
- **SCA and MM**: Sequential convex approximation and majorization-minimization to linearize non-convex constraints (rate, EH, SINR).
- **Robust optimization**: S-procedure (LMI) and Bernstein-inequalities ensure QoS/outage under bounded/statistical CSI error [2310.02564][2412.08032].
- **Hybrid DRL/FL schemes**: Multi-agent deep RL (with parametrized sharing, federated DDPG) for multi-MF-RIS and multi-agent scenarios, handling both continuous (amplitude/phase/EH ratios/location) and discrete (mode switching) controls, with centralized or federated aggregation [2601.00538][2501.11079].
- **Channel estimation**: DFT-structured pilot design and AO-based RIS beam pattern selection to achieve CRLB-optimal channel estimation under significant thermal noise [2501.10657].

These algorithmic frameworks are shown to converge rapidly and to deliver performance that closely tracks theoretical outer bounds across a range of MF-RIS architectures [2412.01251][2601.00538][2501.11079].

## 6. Applications and Future Challenges

MF-RIS are pivotal in emerging 6G and beyond strategies, spanning:
- **Downlink NOMA/RSMA/OFDMA**: Full-space, flexible DoFs, enabling optimal user decoding order and spectral reuse [2301.13630][2405.16257][2310.02562].
- **Physical layer security**: Simultaneous directional amplification and jamming, outperforming all prior passive and dual-mode schemes [2405.10514][2512.19109].
- **Non-terrestrial platforms (LEO, UAVs)**: Dynamic joint optimization of MF-RIS configuration, relay path selection, and power control to maximize energy- and spectral-efficiency in LEO constellations and drone-assisted access [2501.11079][2512.19109].
- **SWIPT/ISAC/AirComp**: Joint waveform, RIS coefficient, and resource optimization for ultra-reliable multi-modal RF services [2412.01251][2410.06584][2405.16257][2506.01051][2402.05847].
- **Cell-free and mmWave networks**: MF-RIS as distributed virtual LoS relays, reducing base station array size while preserving ultra-rich spatial coverage [2405.16257].

Outstanding research frontiers include robust near-field ISAC models, integration of AI-based real-time control, wireless power self-sustainability (fully battery-less MF-RIS), holistic electromagnetic–IT co-design, mitigation of mutual coupling and non-idealities, and global RIS deployment strategies in massive multi-agent and non-stationary scenarios [2410.06584][2601.00538][2405.16257].

---

**References:**  
[2301.13630]  
[2405.16257]  
[2310.02564]  
[2310.02562]  
[2501.10657]  
[2506.01051]  
[2406.06343]  
[2512.19109]  
[2412.08032]  
[2412.01251]  
[2410.06584]  
[2402.05847]  
[2405.10514]  
[2501.11079]  
[2601.00538]

Source: https://www.emergentmind.com/topics/multi-functional-reconfigurable-intelligent-surface-mf-ris