MF-RIS for Advanced 6G Wireless Networks

Updated 9 January 2026

The paper introduces MF-RIS that simultaneously supports reflection, transmission, amplification, and energy harvesting, surpassing conventional RIS limitations.
It leverages advanced signal processing and alternating optimization techniques to maximize objectives like sum-rate, energy efficiency, and secrecy in NOMA networks.
Performance benchmarks indicate up to 73% higher sum-rate and improved sensing and energy self-sustainability compared to traditional RIS architectures.

A multi-functional reconfigurable intelligent surface (MF-RIS) is an engineered electromagnetic metasurface that provides simultaneous wave reflection, transmission (refraction), amplification, and often energy harvesting. MF-RISs go beyond conventional single-functional RISs by integrating active circuitry and multi-path propagation mechanisms, enabling full-space coverage, overcoming double-fading attenuation, supporting dynamic resource allocation, and facilitating advanced physical-layer functionalities in wireless communication, sensing, and computation systems (Zheng et al., 2023, Ni et al., 2024, Wang et al., 2023).

1. Physical Architecture and Signal Processing Model

An MF-RIS comprises $M$ meta-elements capable of partitioning and processing the incident RF signal into several branches:

Reflection Branch: Each element applies a complex coefficient $\sqrt{\beta_m^r} e^{j\theta_m^r}$ to the reflected signal. Reflection is steered via phase and amplitude control, often facilitated by a tunable impedance and integrated low-noise amplifier (LNA).
Transmission Branch: Simultaneously, a transmission coefficient $\sqrt{\beta_m^t} e^{j\theta_m^t}$ modifies the transmitted (refracted) wave, again allowing phase and amplitude optimization.
Amplification: The active load (amplifier) provides gain, allowing $\beta_m^{r}, \beta_m^{t} > 1$ , while preserving energy conservation via $\beta_m^{r} + \beta_m^t \leq \beta_{\max}$ .
Energy Harvesting (optional): Some architectures implement a harvesting mode (H-mode), with $\alpha_m=0$ , routing incident power to a rectifier circuit to support self-sustainability; in signal mode (S-mode, $\alpha_m=1$ ), the element performs reflection/transmission/amplification (Wang et al., 2023, Mukherjee et al., 1 Jun 2025, Shen et al., 19 Jan 2025).

The MF-RIS coefficient matrices are:

$\Theta_k = \mathrm{diag}\left(\sqrt{\beta_1^k}e^{j\theta_1^k}, \dots, \sqrt{\beta_M^k}e^{j\theta_M^k}\right),\quad k \in \{ r, t \}$

The joint constraint is $\beta_m^r + \beta_m^t \leq \beta_{\max}$ , with $0 \leq \beta_m^p \leq \beta_{\max}$ .

The end-to-end signal model for a BS-RIS-user channel, with $N$ BS antennas and $K$ users, is:

$y_k = \left(h_k^H + g_k^H \Theta_k H\right)x + g_k^H \Theta_k n_s + n_k$

where $h_k$ is the direct BS-user channel, $g_k$ the RIS-user channel, $H$ the BS-RIS channel, $n_s$ RIS noise (amplified), and $n_k$ user receiver noise.

2. Optimization Frameworks and Control Protocols

MF-RIS-assisted systems typically formulate multi-objective non-convex problems—maximizing sum-rate, energy efficiency (EE), secrecy rate, or sensing SINR—under transmit power, RIS power, element constraints, and QoS constraints (Zheng et al., 2023, Zheng et al., 2024, Wang et al., 2023):

A generic sum-rate maximization in NOMA downlink is:

$\max\,\sum_{k=1}^K R_k \ \text{s.t.}~\sum_{k=1}^K \|w_k\|^2 \leq P_{\max},~\sum_{k=1}^K (\|\Theta_k H w_k\|^2 + \|\Theta_k\|_F^2 \sigma_s^2 ) \leq P_o,\ \beta_m^r + \beta_m^t \leq \beta_{\max},\,R_k \geq R_k^{\min}$

where $w_k$ is the BS beamforming vector for user $k$ .

Joint optimization is predominantly solved via alternating optimization (AO) strategies, separating beamforming and MF-RIS coefficients subproblems. Convex relaxation (SDR), SCA, rank-one penalties (e.g., SROCR), and LMI transformations (S-procedure, Bernstein-type inequalities for imperfect CSI) are embedded in most solution methods (Zheng et al., 2023, Zheng et al., 2024, Wang et al., 2023). Control protocols include energy splitting (ES), mode switching (MS), and time switching (TS) for dynamic allocation of reflection/transmission/amplification functions (Ni et al., 2024, Han et al., 2024).

3. Deployment Challenges and Practical Constraints

Key challenges in deploying MF-RIS include:

Hardware Complexity: Integration of three-layer structures (reflective, amplification, refractive), tunable impedance networks, low-noise amplifiers, and microcontrollers increases circuit design and power budget (Ni et al., 2024, Zheng et al., 2023).
Amplifier Nonlinearity and Thermal Noise: Amplification incurs additional thermal noise, demanding careful gain budgeting to avoid excessive SNR degradation (Wang et al., 2023, Pan et al., 18 Jan 2025).
CSI Acquisition and Control Overhead: Full-space coverage and per-element amplitude/phase control require precise CSI of all principal channels and frequent reconfiguration, leading to increased signaling overhead (Zheng et al., 2023).
Energy Sustainability: Power-hungry amplification must be supported by local harvesting or grid supply; nonlinear rectification models are used to determine harvesting feasibility (Wang et al., 2023, Mukherjee et al., 1 Jun 2025, Shen et al., 19 Jan 2025).
Element/Phase Quantization and Coupling: Discrete phase shifter resolution and amplitude-phase coupling (varactor diodes) introduce non-idealities and demand joint optimization (Ni et al., 2024).

4. Multifunctionality: Communication, Sensing, and Beyond

MF-RISs unlock diverse physical-layer integration capabilities, enabling:

Full-Space Communication: By simultaneous reflection, transmission, and amplification, MF-RIS achieves coverage for all surrounding users (Zheng et al., 2023, Ni et al., 2024). This is critical for LoS blockage scenarios and spatial diversity.
Physical-Layer Security: MF-RIS optimizes the secrecy rate via spatial jamming and artificial noise generation, outperforming STAR-RIS and active RIS in secrecy outage and throughput (Pei et al., 2024, Zhao et al., 22 Dec 2025).
SWIPT and Energy Harvesting: MF-RIS partitions its aperture to allocate subsets for reflection, transmission, and energy harvesting, optimizing the trade-off between bit error rate and harvested power in chaotic noncoherent SWIPT settings (Mukherjee et al., 1 Jun 2025).
Integrated Sensing and Communications (ISAC): MF-RIS supports joint beamforming for both communication and radar sensing, maximizing ISAC SINR; protocols such as ES outperform MS, TS, active RIS, passive RIS, and STAR-RIS (Han et al., 2024, Ni et al., 2024).
Over-the-Air Computation and Edge Processing: Channel alignment and beamforming via MF-RIS minimize MSE in distributed computation and enable local sensing-driven reconfiguration (Ni et al., 2024, Ni et al., 2024).

5. Performance Benchmarks and Analytical Insights

Simulation results across multiple studies confirm the key advantages of MF-RIS:

Spectral Efficiency: MF-RIS achieves 50–73% higher sum-rate and 44–90% higher EE than passive/STAR/active RISs at typical $M$ (elements) and $P_\text{tot}$ (Zheng et al., 2023, Ni et al., 2024, Wang et al., 2023, Zheng et al., 2024).
Secrecy and Diversity: In NOMA networks, MF-RIS secures diversity order $M$ against external eavesdropping, while imperfect SIC eliminates gains for internal eavesdroppers (Pei et al., 2024). Amplification and full-space coverage together are essential.
Self-Sustainability: Closed-form bounds establish the optimal split between harvesting and active elements, and MF-RIS outperforms self-sustainable passive RISs when deployed close to the transmitter (Wang et al., 2023, Mukherjee et al., 1 Jun 2025, Shen et al., 19 Jan 2025).
ISAC Gains: ES protocols in MF-RIS provide sensing SINR improvements of 52.2–73.5% compared to all conventional RIS types, and their gains saturate beyond a threshold number of sensing elements (Han et al., 2024).
Beamforming Design: DFT-based LS estimation with AO yields CRLB-optimal channel estimation MSE under thermal noise and pilot limitations (Pan et al., 18 Jan 2025).
Multi-MF-RIS Agents: Multi-agent hybrid DRL architectures with parameter sharing (e.g., PMHRL, FEMAD) efficiently solve EE maximization over multiple MF-RISs in dynamic NOMA or LEO networks (Kuo et al., 2 Jan 2026, Shen et al., 19 Jan 2025).

6. Implementation Platforms, Prototypes, and Future Directions

Thin-film inkjet-printed MF-RIS prototypes demonstrate the viability of low-cost, mechanically flexible metasurfaces with per-element phase/harmonic control for both front (reflection) and back (refraction) operation, realizing harmonic multiplexed beam steering (Xie et al., 2024). Application spaces include:

Device-Free Localization: Harmonic fingerprints generated by MF-RIS can facilitate high-precision non-intrusive sensing (Xie et al., 2024).
UAV and LEO Platforms: MF-RIS-equipped UAVs and satellites autonomously optimize reflection/jamming and signal delivery, leveraging DRL and federated learning for trajectory and resource management (Zhao et al., 22 Dec 2025, Shen et al., 19 Jan 2025).
Near-Field ISAC: Ongoing research targets spherical wavefront models, adaptive beamforming, and AI-driven real-time control in advanced 6G deployments (Ni et al., 2024).

7. Summary Table: MF-RIS vs. Conventional RISs

Feature	Single-functional RIS (SF-RIS)	STAR-RIS (DF-RIS)	Active RIS	MF-RIS (Multifunctional)
Reflection	Yes	Yes	Yes (w/ amplifiers)	Yes
Transmission/Refraction	No	Yes	No	Yes
Amplification	No	No	Yes (single-path)	Yes (dual-path)
Energy Harvesting	No	No	Limited	Yes
Coverage	Half-space	Full-space	Partial	Full-space
Sensing Integration	Poor	Moderate	Strong	Strong + local echo
Representative Papers	(Ni et al., 2024, Zheng et al., 2023)	(Ni et al., 2024, Zheng et al., 2023)	(Wang et al., 2023, Han et al., 2024)	(Zheng et al., 2023, Zheng et al., 2024, Wang et al., 2023)

8. Design Guidelines and Practical Recommendations

Element Partitioning: Optimally allocate elements between signal, harvesting, and sensing roles for self-sustainability and multi-objective trade-offs (Wang et al., 2023, Mukherjee et al., 1 Jun 2025).
Deployment Location: Place MF-RIS closer to the transmitter for maximal energy harvesting and amplification capacity (Wang et al., 2023, Shen et al., 19 Jan 2025, Han et al., 2024).
Control Protocols: Prefer energy splitting (ES) for robust ISAC/SWIPT gains; utilize mode/time switching for legacy integration and complexity reduction (Ni et al., 2024, Han et al., 2024).
Amplification Noise Budgeting: Control $\beta_{\max}$ such that added RIS noise does not offset amplification benefits (Wang et al., 2023, Pan et al., 18 Jan 2025).
CSI Acquisition: Invest in estimation accuracy and robust beamforming, especially for large-scale surfaces and imperfect environments (Zheng et al., 2024, Pan et al., 18 Jan 2025).
AI-Driven Control: Algorithmic advances in deep reinforcement learning (DRL), especially multi-agent architectures with parameter sharing, are critical for real-time MF-RIS optimization in emerging applications (Kuo et al., 2 Jan 2026, Shen et al., 19 Jan 2025, Zhao et al., 22 Dec 2025).

MF-RIS constitutes the current frontier in metasurface-aided wireless communications, harnessing simultaneous multi-wave propagation and active amplification to overcome major limitations of conventional RISs in double-fading, coverage, energy efficiency, and joint communication-sensing-computation. The physical implementations, algorithmic frameworks, and system-wide integration strategies provided in the cited works (Zheng et al., 2023, Ni et al., 2024, Wang et al., 2023, Zheng et al., 2024, Xie et al., 2024, Han et al., 2024, Pan et al., 18 Jan 2025, Zhao et al., 22 Dec 2025, Kuo et al., 2 Jan 2026, Mukherjee et al., 1 Jun 2025, Pei et al., 2024, Shen et al., 19 Jan 2025, Zheng et al., 2023) reflect a rapidly evolving technology landscape, with documented performance and open research avenues for next-generation (6G/7G) multi-functional wireless networks.