Self-Sustainable RIS: Energy-Neutral Surfaces

• Self-sustainable RISs are autonomous, energy-neutral surfaces that harness ambient RF energy to dynamically manipulate electromagnetic waves.
• Hybrid and functional partitioning architectures enable the balancing of energy harvesting and reconfigurable reflection for varied application scenarios.
• Optimization frameworks like BCD and SSCA ensure effective resource allocation and robust performance under dynamic RF and power constraints.

Self-sustainable Reconfigurable Intelligent Surfaces (ssRISs) are autonomous wireless structures that leverage ambient or dedicated radio frequency (RF) energy to power their reconfigurable circuitry while performing their core function: intelligent electromagnetic wave manipulation. By embedding RF energy harvesting directly into the metasurface, ssRISs eliminate the need for wired power or frequent maintenance, facilitating deployment in locations where cabling or battery replacement is impractical. These systems extend the vision of programmable wireless propagation in next-generation networks toward energy-neutral operation and zero-infrastructure overhead.

1. Fundamental Principles and Architectures

An ssRIS consists of an array of meta-atoms capable of dynamically adjusting their electromagnetic response (phase, amplitude, polarization) to incident wireless signals, governed by a local or external controller. The key differentiators from conventional RISs are (i) their energy autonomy—achieved via integrated RF energy harvesting—and (ii) their possible independence from wired control or power interfaces.

Two primary architectures for ssRIS realization are identified:

• Hybrid Unit Cell Design: Each meta-atom or supercell unites energy harvesting and reconfigurable reflection. For example, ELC-type resonators with PIN-diode switches and a central via achieve programmable reflection (ON: high reflection, OFF: energy absorption) (Ghaneizadeh et al., 2024).
• Functional Partitioning: The surface is divided in space or time into harvesting and reflecting roles via element-splitting or duty-cycle scheduling. In “element-splitting” (ES), some elements harvest while others reflect; in “time-splitting” (TS), all elements alternately harvest and reflect (Li et al., 8 Jan 2026).

Amplified (active) ssRISs further integrate low-power amplifiers into each cell, powered by local harvested RF energy or internal storage, allowing for per-element active gain and improved anti-jamming or coverage range (Cao et al., 2024).

2. Harvesting-and-Reflect Schemes: ES vs TS

Performance analysis of ssRISs fundamentally hinges on the management of two resources: harvested energy and surface area (number of elements).

• Element-Splitting (ES): The surface is partitioned such that a subset M_Hr harvests energy from incoming waves, powering the reflecting subset M_Rf. The self-sustainability constraint is

$\eta P_{tx} \ell_{r,s} N M_{Hr} \geq M_{Rf} P_0,$

where $\eta$ is the per-element harvesting efficiency, $P_{tx}$ is the BS transmit power, $\ell_{r,s}$ is the BS–RIS path-loss, $N$ the number of BS antennas, and $P_0$ the phase-shifter power consumption (Li et al., 8 Jan 2026).

• Time-Splitting (TS): All elements alternate roles with fraction $\tau$ harvesting and $1-\tau$ reflecting. The harvested energy over time $\tau$ must satisfy

$$\tau \eta P_{tx} \ell_{r,s} N M \geq (1-\tau) M P_0,$$

yielding a key ratio $\alpha = P_0 / (\eta P_{tx} \ell_{r,s} N)$.

In the line-of-sight (LOS) channel, the minimum element counts scale differently. For increasing $\alpha$ (i.e., harvesting difficulty), TS requirements grow exponentially, while ES requirements increase only linearly. The upshot is a sharp environment-dependent crossover: TS is preferable in benign, indoor or high-power-of-opportunity settings and moderate data rates; ES dominates when power harvesting is challenging, data rate demands are high, or ultra-low outage is required (Li et al., 8 Jan 2026).

A representative comparison is shown below for $N = 128$, $B = 50$ MHz, $\eta = 0.65$, $P_0 = 2 \mu$W:

$\alpha$	$M_{\text{ES}}^*$ ($R_0$=10 Mbps)	$M_{\text{TS}}^*$ ($R_0$=10 Mbps)	$M_{\text{ES}}^*$ ($R_0$=20 Mbps)	$M_{\text{TS}}^*$ ($R_0$=20 Mbps)
0.1	50	48	70	67
1.0	100	140	140	340

Exponentially increasing TS element counts at high $\alpha$ underscore its unsuitability for harsh, outdoor scenarios.

3. Physical Cell Design and Power Budgeting

The physical enabler for ssRISs is the metasurface energy harvester (MEH), which, in its contemporary form, uses subwavelength periodic structures with embedded rectifiers. The critical metrics are:

• RF-to-DC conversion efficiency: The overall efficiency $\eta_{RF-DC}$ is the product of surface absorption $\eta_{RF-AC}$ and rectification $\eta_{AC-DC}$, with full-wave EM simulations validating cell-level absorption exceeding 80% in harvesting-optimized bands (Ghaneizadeh et al., 2024).
• Hybrid operation: 1-bit or multi-level unit cells are realized using combinations of PIN diodes, allowing switching between near-unity absorption and high-reflection (with 180$^\circ$ phase jump). The absorption and reflection bands are not perfectly overlapping, motivating frequency or polarization splitting strategies.
• Power consumption: Practically, a controller may draw $\approx$12 mW, with each reflect element (switch) consuming $\approx$0.5 mW, and bias/driving networks a few additional mW. Ambient power densities—typically in the 10–1000 nW/cm$^2$ range at sub-6GHz—demand approx. 100 cm$^2$ per element for continuous powering, which is challenging at high element density or higher frequencies. A suggested implementation is surface area splitting, dedicating a large area for harvesting to support a smaller reflecting RIS, with a ratio such that $P_{\text{avail}}/P_{\text{consum}}\approx1$ (Ghaneizadeh et al., 2024).

4. Optimization and Algorithmic Frameworks

Most ssRIS optimization frameworks address the interdependent allocation of surface resources and the scheduling of harvesting and reflection. Advanced techniques include:

• Closed-form expressions for minimum element counts: Formulas for $M_{ES}^*$ and $M_{TS}^*$ as functions of $\alpha$, $R_0$, bandwidth, and power budgets, allow feasibility assessment under both LOS and NLOS channel statistics (with Rayleigh fading/Bernoulli outage models for NLOS) (Li et al., 8 Jan 2026).
• Simultaneous wireless information and power transfer (SWIPT): Multiple architectural approaches, including time-sharing, power-splitting, dynamic splitting, and antenna selection, offer distinct robustness-rate tradeoffs for powering RIS control circuits (Kisseleff et al., 2021).
• Block Coordinate Descent (BCD) and Penalty-based SCA: MIMO downlink with one or more ssRISs employs cyclic updates for beamformer vectors, phase-shift matrices, and amplitude (reflection) coefficients, interleaved with closed-form or convexified subproblems, ensuring convergence and energy neutrality (Wang et al., 2024, Pan et al., 2021).
• Stochastic Successive Convex Approximation (SSCA): For active ssRISs, alternating optimization across the time-split ratio, beamformer, and RIS coefficients accommodates random channel uncertainty, with inner-outer iteration scheduling and Lagrangian surrogate functions (Cao et al., 2024).

5. Experimental Validation and Numerical Results

Simulations and experimental budgets across the referenced works delineate the attainable performance and the trade-off structure.

• Element count scaling: Minimum necessary elements increase with energy-harvesting difficulty, stricter outage, or higher rates. TS element scaling is exponential in challenging conditions, while ES scales linearly. NLOS outage-constrained evaluations reveal that TS exhibits superior channel hardening (lower sensitivity to outage target) but becomes rapidly infeasible in severe scenarios (Li et al., 8 Jan 2026).
• Hybrid cell performance: EM simulations show that dual-function unit cells can achieve $>$80% harvesting efficiency in the “OFF” (harvesting) state and $>$30% reflection efficiency in “ON” (reflecting) state, with $\approx$180$^\circ$ phase offset at resonance. However, harvesting and reflection bands do not fully overlap, limiting simultaneous performance (Ghaneizadeh et al., 2024).
• Power budget feasibility: Under empirical urban RF spectra, the required per-element area to sustain practical power budgets is large for continuous high-reconfigurability, motivating alternate duty cycles or hybrid area-division designs (Ghaneizadeh et al., 2024).
• Multi-ssRIS deployments: Double ssRIS configurations consistently reduce BS transmit power relative to single-ssRIS and no-RIS setups, with up to 12 dB savings at moderate element counts. Performance saturates as element count increases due to rising per-element circuit power (Wang et al., 2024).
• Self-configuration and storage: Autonomous modes (e.g., ARES) using local probing and finite codebooks can achieve sum rates within 5–10% of a centralized-CSI benchmark, with battery storage ensuring high reliability even at moderate traffic and low per-element power (Albanese et al., 2023).

6. Practical Design Guidelines and Open Challenges

For robust and feasible ssRIS deployment, the following principles and challenges dominate:

• Regime selection: Deploy TS-based ssRIS in indoor, high-power, or low-rate environments; ES in outdoor, power-limited, or highly reliable use-cases (Li et al., 8 Jan 2026).
• Surface area allocation: Where per-element power is high or ambient RF density is low, split the surface between harvesting and reflecting subpanels; optimize ratio according to ambient environment and switching power (Ghaneizadeh et al., 2024).
• Unit cell engineering: Align reflection and harvesting bands or polarizations, and reduce controller/switch power via ultra-low-power digital/ASIC controllers or “non-volatile” MEMS meta-atoms (Ghaneizadeh et al., 2024).
• Adaptive operation: Develop protocols and hardware for environmental adaptation, including power-aware scheduling and recalibration in response to RF fluctuations (Ghaneizadeh et al., 2024, Albanese et al., 2023).
• Scaling limits: Account for “diminishing returns” in performance as the reflecting element count increases—higher circuit power reverses expected beamforming and SNR gains (Wang et al., 2024).
• Standardization: Adopt measurement methodologies for $P_{\text{avail}}$ and $P_{\text{consum}}$, aligning with emerging RIS standards (e.g., ETSI GR RIS series) (Ghaneizadeh et al., 2024).

Unaddressed challenges include fully polarization-insensitive and frictionless multiband harvesting, high-order (>2-bit) phase quantization without excessive circuit power, harvesting performance under highly variable/incoherent RF incidence, and practical large-scale prototyping consistent with 6G deployment constraints.

7. Impact and Outlook

Self-sustainable RIS architectures are positioned as a foundational technology for large-area, zero-power programmable wireless environments. They are critical for the realization of “Internet-of-Surfaces” paradigms, providing ubiquitous, infrastructure-free manipulation of propagation in next-generation radio systems (Albanese et al., 2023). While initial prototypes and analytical scaling laws validate architectural feasibility, further research is necessary to optimize the intricate trade-off surface defined by harvesting efficiency, power consumption, reliability constraints, deployment topology, and real-world RF ambient variability.

References:

• (Li et al., 8 Jan 2026, Ghaneizadeh et al., 2024, Cao et al., 2024, Kisseleff et al., 2021, Pan et al., 2021, Wang et al., 2024, Albanese et al., 2023)