Aerial Active RIS: Reconfigurable Amplified Platforms

• Aerial active RIS are reconfigurable platforms that mount active metasurfaces on UAVs to amplify and digitally control electromagnetic waves.
• They enhance wireless communications, 6G backhaul, SAR imaging, and IoT connectivity by compensating for double-path loss and enabling precise beamforming.
• Advanced PCB designs and non-convex optimization frameworks coordinate amplifier gain, phase control, and UAV placement to ensure robust 3D coverage.

Aerial active reconfigurable intelligent surfaces (RIS) are platforms, typically mounted on unmanned aerial vehicles (UAVs) or similar airborne assets, that integrate active (amplifying) reconfigurable metasurfaces with mobility to dynamically manipulate electromagnetic (EM) waves for communication, sensing, and imaging applications. Unlike conventional passive RIS, aerial active RIS provides electronic amplification and filtering at each unit cell, compensates for double-path-loss in multi-hop reflectivity scenarios, and enables sophisticated beamforming with both phase and amplitude control. Aerial integration offers additional spatial degrees of freedom and robust line-of-sight (LoS) links in three-dimensional environments. This class of technology supports emerging use cases in wireless relay, 6G backhaul, synthetic aperture radar (SAR) imaging, and IoT networks, delivering substantially enhanced link budgets and spatial coverage (Wu et al., 2024, Jeon et al., 25 Jan 2026, Sun et al., 2024, Zhao et al., 5 Jan 2025).

1. Architectural Principles and Unit-Cell Design

Aerial active RIS platforms consist of programmable metasurface arrays chemically processed on multi-layer printed circuit boards (PCBs), employing amplifying RF front-ends, band-pass filters, and digitally controlled phase-shift networks. The reference design (Wu et al., 2024) specifies:

• Top layer: square microstrip patch (30.87 mm × 30.87 mm) on FR4.
• Middle layer: Rogers RO4350B (7.5 mm) with orthogonal slots for polarization multiplexing and microstrip routing.
• Bottom layer: solid copper ground on FR4, separated by a 30 mm air gap.
• RF chain: incident x-polarized EM wave couples into the patch, routed through a band-pass filter (BFCN-3010+), cascaded low-noise amplifier (LEE-39+), and PIN-diode based 2-bit phase-shifter.
• Each cell achieves up to +21 dB reflection amplitude with activated amplifier, significant out-of-band rejection (>20 dB at ±400 MHz from center), and 2-bit phase quantization ({0°, 90°, 180°, 270°}) with minimal insertion loss.

A key feature is the integration of lumped-element DC-RF decoupling (e.g., 2200 pF DC-blocks, 100 nF RF bypass, bias resistors) for robust biasing and noise suppression. The multi-element array (4×8 sub-arrays in reference design) leverages Wilkinson-style power-dividing/combing networks, minimizing amplifier count and hardware cost.

2. Channel Model, Amplification, and Beamforming

Aerial active RIS platforms operating at altitude (e.g., $H\in[120,240]\rm\,m$) typically enable LoS links from ground base stations to the RIS and onward to aerial or ground users. Channel models for active RIS involve double-fading due to source-RIS and RIS-user propagation, each subject to free-space path loss, Rician fading, and array response vectors (Jeon et al., 25 Jan 2026, Zhao et al., 5 Jan 2025). Signal models include:

• RIS signal processing: Each element applies phase shift $\theta_n\in[0,2\pi)$ and amplitude gain $\alpha_n > 1$ via active RF amplification.
• Reflected and amplified signal: $$\mathbf{y}_{ra} = \mathbf{A} \Theta \mathbf{H} \mathbf{x} + \mathbf{A} \Theta \mathbf{n}_a + \mathbf{n}_s$$, where amplifier noise $$\mathbf{n}_a \sim \mathcal{CN}(0, \sigma_a^2 \mathbf{I})$$ and static surface noise $\mathbf{n}_s$ contribute to the overall SNR.
• End-to-end SNR for user $m$:

$$\gamma_m = \frac{P_m G_s \left| \mathbf{h}^* \mathbf{A} \Theta \mathbf{H} \mathbf{v}_m \right|^2 }{ \sigma_a^2 \| \mathbf{A} \mathbf{h} \|^2 + \sigma^2}$$

Joint phase and gain optimization leads to coherent beamforming around a phase-alignment point $\bar{\boldsymbol\rho}$, supporting multi-user 3D coverage.

For SAR imaging (Sun et al., 2024), ARIS mobility effectively synthesizes a virtual aperture exceeding the physical array, enabling high spatial-resolution images. The ARIS-optimized reflection matrix $\Phi_n$ at each UAV position maximizes echo SNR at the radar receiver, using fractional programming (FP) and majorization-minimization (MM) to solve for optimal phase/amplitude coefficients under amplifier noise and total power constraints.

3. Optimization Frameworks and Algorithms

The operation of aerial active RIS networks requires multi-variate, non-convex optimization for platform deployment, array configuration, amplifier bias, and beamformer design. Representative approaches include:

• Energy-efficiency optimization (Jeon et al., 25 Jan 2026): The total objective is to minimize

$$\sum_m P_m + [\alpha^2(NM\beta_s\sum_m P_m + N \sigma_a^2) + N P_E]$$

subject to rate matching, source and RIS power budgets, per-element gain bounds, and coverage constraints. Block-coordinate (alternating) optimization iteratively solves for RIS placement, subarray partitioning, phase-alignment, amplification gain, and transmit powers.

• Beamforming and power allocation for active STAR-RIS (Zhao et al., 5 Jan 2025): Joint beamforming, UAV trajectory, and device power assignment are solved by an alternating optimization (AO) framework, penalty-based rank-one relaxation for combined phase/amplification variables, and successive convex approximation for trajectory and power allocation.
• SAR scene optimization (Sun et al., 2024): Active reflection coefficients are computed per slow-time slot using FP and MM techniques, balancing SNR maximization against thermal/amplifier noise and hardware envelope constraints.

4. System Performance and Empirical Findings

Empirical validation demonstrates substantial gains for aerial active RIS platforms relative to passive RIS or AF relays. Key results:

System	Gain over Passive RIS	Representative Metrics
Aerial AF-RIS (4×8 at 3 GHz) (Wu et al., 2024)	+15 dB SNR improvement (link test)	17–21 dB main-lobe gain, <4 W consumption, 30 mm air gap
Active RIS backhaul (N=120–300, H=120–240m) (Jeon et al., 25 Jan 2026)	30–36 dB reduction in total power	Full 3D UAV coverage, feasible with ≤40 dBm RIS supply
SAR with ARIS (M elements, a_max=20) (Sun et al., 2024)	10–20 dB higher SNR, sharper images	Imaging area 30×30m, SNR scaling with M and P_ARIS
UAV active STAR-RIS NOMA (Zhao et al., 5 Jan 2025)	Superior sum-rate, connectivity	Power compensation, high-quality channel construction

Performance metrics include main-lobe gain (up to 21 dB), side-lobe suppression (–12 to –18 dB), out-of-band rejection (>20 dB at ±400 MHz), and enhanced SNR/scaling with array size and amplifier gain. Aerial integration (~1.2 kg, <4 W worst-case dissipation) is validated for practical UAV deployment, with 30+ hours autonomous operation at 100 Wh battery capacity.

5. Deployment Considerations and Integration

Airborne AF-RIS systems are designed for compactness, energy efficiency, and robust mechanical operation:

• Mechanical: PCB stacks in composite frames, vibration-isolated, with aerodynamic fairings for wind loads (~0.2 m² frontal area).
• Thermal: Total dissipation (<4 W for 32-element AF-RIS; typical ~3.4 W) managed by convection and heat spreaders.
• Power and control: Two DC bias rails (e.g., 12 V, 1–7 V) supplied by DC–DC converters; control via SPI/GPIO for PIN diodes and amplifiers, supporting multiplexing for >100 lines.
• Portability: 550×360×40 mm, estimated 1.2 kg; compliant with small UAV or balloon payload constraints.
• Control: MCU/FPGA systems for real-time reconfiguration; multi-mode operation for beam-steering, filtering, and power adaptation based on mission profile.

A plausible implication is that mechanical robustness and battery autonomy are not limiting factors for practical aerial AF-RIS deployment in most UAV scenarios.

6. Applications and Future Directions

Aerial active RIS platforms address several use cases:

• Wireless relay and 6G backhaul: Energy-efficient aerial backhaul for UAV base stations, full 3D coverage, and blockage mitigation in urban environments (Jeon et al., 25 Jan 2026).
• SAR imaging: Enhanced spatial resolution and SNR for stationary and mobile radar platforms, leveraging ARIS mobility and active reflection optimization (Sun et al., 2024).
• IoT and NOMA networks: Multi-user channel construction, power compensation, non-orthogonal access, and trajectory-controlled connectivity via active STAR-RIS (Zhao et al., 5 Jan 2025).
• Frequency-selective relays: In-band signal enhancement and out-of-band suppression via integrated filters, supporting anti-interference in congested spectra (Wu et al., 2024).

Future directions suggested in the data include robust design under imperfect CSI, dynamic placement and adaptive beamforming for moving users, joint digital/analog optimization (including antenna tilt), and multi-hop active RIS chains for extended coverage. Design optimization must also balance amplifier noise against gain (trade-off governed by closed-form $\alpha^*$) and account for distributed/robust algorithms under real-world perturbations.

7. Technical Limitations and Design Trade-offs

Amplification in aerial RIS entails intrinsic trade-offs:

• Thermal and amplifier noise scale with gain $\alpha$, so excessive amplification reduces net SNR and increases power draw (Sun et al., 2024, Jeon et al., 25 Jan 2026).
• Hardware budgets per element and total array must be considered; total RIS supply (e.g., $P_{\max,a}$) sets a hard upper limit on achievable reflected power.
• Finite phase quantization (e.g., 2-bit) will impact beamforming resolution and side-lobe suppression.
• Power-dividing/combining networks introduce additional insertion loss and complexity in scaling arrays.

The evidence strongly supports that active amplification is necessary to compensate for the multiplicative path loss in double-hop RIS systems, particularly for aerial deployments where LoS and dynamic placement amplify the benefit. However, designers must optimize amplifier bias, array partitioning, and phase/gain control to maintain energy efficiency and maximize SNR or throughput. This integrative approach is essential for realizing sustainable and high-performance aerial RIS operations in next-generation wireless infrastructure.

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• "A wideband amplifying and filtering reconfigurable intelligent surface for wireless relay" (Wu et al., 2024)
• "Ampli-Flection for 6G: Active-RIS-Aided Aerial Backhaul with Full 3D Coverage" (Jeon et al., 25 Jan 2026)
• "Active Reconfigurable Intelligent Surface Empowered Synthetic Aperture Radar Imaging" (Sun et al., 2024)
• "Aerial Active STAR-RIS-Aided IoT NOMA Networks" (Zhao et al., 5 Jan 2025)