Fluid-Antenna Systems Overview

• Fluid-antenna systems are reconfigurable antennas that use fluids, movable metallic pixels, or meta-atoms to dynamically vary their radiating aperture.
• They leverage architectures like liquid-metal channels and pixel arrays to enable adaptive beamforming, interference avoidance, and optimal channel sampling.
• FAS enhance wireless performance in 6G, IoE, and UAV networks by providing improved diversity, capacity, and energy harvesting through dynamic reconfigurability.

Fluid-antenna systems (FAS) constitute a class of reconfigurable antennas whose radiating elements are realized by conductive or dielectric fluids, movable metallic pixels, or meta-atom arrays. The defining feature is dynamic spatial and/or shape reconfigurability of the radiating aperture, enabling the antenna to alter its position, geometry, or feeding configuration in real time. This flexibility fundamentally distinguishes FAS from traditional fixed-element arrays and underpins a suite of new spatial, spectral, and physical-layer capabilities, including selection-based spatial diversity, adaptive beamforming, interference avoidance, and optimal channel sampling—even within highly compact form factors. FAS are poised to play a pivotal role in future 6G and beyond, IoE, ISAC, SWIPT, and UAV-centric networks due to their ability to combine multiplexing, diversity, and application-adaptable operation within a single, software-controlled RF aperture (Wu et al., 5 Dec 2024); (Hong et al., 16 Jun 2025); (Lu et al., 6 Jan 2025).

1. Physical Principles and Enabling Architectures

FAS rely on reservoirs, microfluidic channels, arrays of conductive meta-atoms, or programmable pixel layers that can reconfigure the effective aperture by moving (or activating) the radiating medium among a discrete or continuous set of ports (Wu et al., 5 Dec 2024); (Zhang et al., 8 Jun 2024). The system may involve:

• Liquid-metal antennas: Conductive fluids (e.g., Galinstan or eutectic gallium–indium) are displaced using micro-pumps, shape-memory-actuator mechanisms, or electrostatic control along millimeter-scale channels, forming the active radiating element at selectable positions (Wu et al., 5 Dec 2024); (Lu et al., 6 Jan 2025); (Psomas et al., 2023).
• Pixel-based or meta-fluid arrays: Arrays of metallic patches (“pixels” or “meta-atoms”) with electronic switches (e.g., PIN diodes) form a rapid-switching FAS. These can provide dozens to hundreds of distinct radiating states over a λ to few λ aperture, with state transitions on the μs timescale (Zhang et al., 8 Jun 2024); (Liu et al., 15 Sep 2025).
• Dielectric- or phase-change media: The effective dielectric loading is adjusted by flowing or displacing fluids of variable permittivity around a metallic trace (Wu et al., 5 Dec 2024).
• Feed control: The excitation is switched among ports/programmed pixels, or spatial feeding profiles are induced, to selectively excite basis eigenmodes of the physical aperture (Lu et al., 6 Jan 2025).

The architecture may be realized in 1D (linear tracks), 2D (planar matrices), or via programmable meta-surfaces (e.g., meta-fluid antennas) (Liu et al., 15 Sep 2025).

2. Mathematical Models and Channel Characterization

FAS channel models must account for small-scale fading, spatial correlation, and fluid/material geometry:

• Jakes-type spatial correlation: For a linear (or 2D) FAS with N ports distributed over length Wλ, the channel vector $\mathbf{h} = [h_1,\ldots,h_N]$ exhibits spatial autocorrelation

$$\mathbb{E}[h_n h_m^*] = J_0\left(2\pi \frac{|n-m| W}{N-1}\right)$$

where $J_0$ is the zeroth-order Bessel function (Hong et al., 16 Jun 2025); (Lu et al., 6 Jan 2025).

• Eigenmode expansion: Antenna behavior is governed by eigenmodes $\mathbf{E}_n$ (solutions of the vector wave equation under reconfigurable boundary conditions), with real-time adaptation via spatial boundary or feeding changes (Lu et al., 6 Jan 2025).
• Selection combining gain: The FAS selects the port $n^* = \arg\max_n |h_n|^2$, yielding an effective received SNR $\gamma_{\max} = \max_n |h_n|^2 P / \sigma^2$, and achieving diversity order typically limited by the effective rank of the spatial correlation matrix, not just N (Zhu et al., 10 Sep 2025).
• Continuous motion: For continuous FAS (CFAS), the SIR or SNR process is a stationary random field $g(x)$ with correlation $J_0(2\pi (x-x')/\lambda)$; level-crossing rate (LCR) and fade-duration (AFD) statistics can be derived in closed form (Psomas et al., 2023).

Nontrivial extension to finite-scattering (geometric) and wideband models is achieved by representing the FAS as a beamformer over a set of tracked rays or via a 2D/3D field-response (Hong et al., 7 Mar 2025).

3. Performance Limits: Diversity, Capacity, and Outage Behavior

FAS offer unconventional spatial diversity and robust outage performance, subject to spatial correlation and aperture constraints:

• Outage probability: For Rayleigh fading, the FAS post-selection outage is

$$P_{\text{out}}(\gamma_{\mathrm{th}}) = \Pr(\max_n |h_n|^2 P / \sigma^2 < \gamma_{\mathrm{th}})$$

For uncorrelated branches, this yields classic order-N diversity; when ports are highly correlated (small W), the performance saturates to an effective diversity order $N_{\text{eff}}$ set by the channel eigenvalue spectrum (Khammassi et al., 2022); (Zhu et al., 10 Sep 2025).

• Ergodic capacity: The selection gain enhances ergodic capacity, with increases up to ~70% over fixed single-port antennas in representative scenarios, especially for moderate N and λ-scale apertures (Wu et al., 5 Dec 2024); (Wong et al., 2020).
• Diversity scaling law: Asymptotic analysis establishes that the FAS diversity gain, coding gain, and rate improvement all scale with the effective spatial rank $N_{\mathrm{eff}} \approx 2W+1$ (for normalized aperture W in wavelengths) regardless of additional port density beyond this threshold (Zhu et al., 10 Sep 2025).
• Saturation effect: Increasing N for fixed W ultimately yields diminishing returns; only enlarging the aperture can substantially increase $N_{\mathrm{eff}}$ and thereby further enhance diversity and error exponent (Zhu et al., 10 Sep 2025); (Khammassi et al., 2022).
• Comparison with MRC: FAS with sufficient N and/or W can outperform conventional multi-antenna MRC in both outage and capacity, using only a single RF chain (Wong et al., 2020); (Wong et al., 2020).

4. Adaptive Beamforming and Reconfigurable Eigenmode Control

Unlike rigid phased arrays or RIS, FAS leverages shape, boundary, and feed reconfiguration at the resonant eigenmode level:

• Eigenmode-resonant beamforming: Continuous adaptation of eigenmodes via spatially tuned boundary conditions (Dirichlet/Neumann/Robin) or feeding positions enables flexible beam steering and null placement without resorting to phased array summation, resulting in highly agile and hardware-efficient beam patterns (Lu et al., 6 Jan 2025).
• Parity and modal symmetry: FAS exploit modal parity (even/odd) and symmetry axes as additional resonant degrees of freedom, allowing for tailored far-field patterns (e.g., dipole vs. hoop) and on-demand nulls or multibeam forms with minimal hardware (Lu et al., 6 Jan 2025).
• Prototype demonstrations: Plasma-lamp, microfluidic, or pixel-based FAS designs have validated wide-area radiation pattern reconfiguration (~10–30 dB beam/null agility) across broad frequency bands (2.4–30 GHz) in both liquid and non-liquid implementations (Lu et al., 6 Jan 2025); (Zhang et al., 8 Jun 2024).
• Ultra-fast reconfiguration: Pixel/meta-fluid systems with PIN-diode switching achieve μs-scale state changes, supporting packet-to-packet adaptation under rapid channel fading (Zhang et al., 8 Jun 2024); (Liu et al., 15 Sep 2025).

5. Application Frameworks: Networking, Sensing, Localization, and SWIPT

FAS unlock a range of novel networking and sensing architectures:

• Fluid antenna multiple access (FAMA): Multi-user interference mitigation via dynamic port selection—slow FAMA (block-based) or fast FAMA (symbol-based)—enables CSI-free, scalable, collision-resilient access in ultra-dense networks (Liu et al., 15 Sep 2025); (Hong et al., 16 Jun 2025).
• 6G and ISAC: FAS contribute to 6G integrated sensing & communications by enabling agile tradeoffs between communication and sensing SNR, adaptable ISAC Pareto frontiers, and precise control of angle/range estimation CRB via joint optimization of FAS position, beamforming, and system parameters (Zou et al., 9 May 2024); (Zhou et al., 30 Sep 2024).
• Energy harvesting and SWIPT: Both joint position–beamforming optimization and continuous element travel allow simultaneous maximization of downlink rate and harvested energy, yielding up to 40% energy-harvesting gains versus fixed arrays (Zhou et al., 16 Jul 2024); (Zhang et al., 23 Oct 2025).
• UAV and indoor deployments: Adaptive FAS port/trajectory planning in UAV or indoor environments enables sub-degree precision in multi-target sensing, robust signal/rate gains, and low-latency adaptation in constrained geometries (Zhu et al., 21 Nov 2025); (Zhang et al., 26 Sep 2025); (Zhang et al., 18 Sep 2025).
• Localization: FAS port correlation structure is directly exploited for high-resolution RSSI-based positioning via MLE-based joint estimation, matching conventional multi-antenna accuracy at reduced hardware and feedback overhead (Liu et al., 2 Mar 2025).

6. Implementation, Design Constraints, and Practical Algorithms

Realizing FAS in practice involves a multidisciplinary set of challenges and corresponding algorithmic innovations:

• Materials and hardware: Fast-switching, low-loss alloys (GaInSn), robust microfluidics, or PIN-diodes for pixel arrays; high spatial precision in fluid/port actuation; field-programmable switch networks; matched input impedance and isolation; loss mitigation at mmWave (Wu et al., 5 Dec 2024); (Zhang et al., 8 Jun 2024).
• Aperture/port optimization: Efficient geometric/policy-gradient algorithms (e.g., GRPO, AO, PSO) tailored to non-convex, real-time design of port positions, beamforming, and power, with computational reductions of up to 83% over classical approaches in large-scale indoor layouts (Zhang et al., 18 Sep 2025); (Zhou et al., 16 Jul 2024).
• Channel estimation: Compressed-sensing, low-overhead pilot design, and machine-learning models for port selection and channel prediction in the presence of estimation overhead and time-varying conditions (Zhang et al., 8 Jun 2024); (Hong et al., 16 Jun 2025).
• Coding and scheduling: Joint port selection and code/beam allocation, robust to switching delays, using coded modulation or space-time rotation schemes to restore or maximize achievable diversity even under port feedback or actuation delays (Psomas et al., 2022); (Hong et al., 7 Mar 2025).
• Standardization and control: Control-protocol extensions (e.g., NETCONF/YANG with fluid state variables), integration with SDN/NFV, and compatibility with 5G NR and massive MIMO standards (Wu et al., 5 Dec 2024).

7. Open Challenges and Emerging Research Directions

FAS pose several research and implementation challenges:

• Effective aperture vs. port density: Design should prioritize increasing physical aperture W over simply increasing N, due to diversity saturation effects (Zhu et al., 10 Sep 2025).
• Fluid/material dynamics: Modeling and compensating for nonidealities in fluid flow, mechanical tolerance, conduction loss, and temperature dependence (Wu et al., 5 Dec 2024).
• Control and feedback: Real-time, distributed optimization under sensing/communication/actuation delays, scalable to hundreds of ports, possibly with AI-driven (e.g., deep RL) policies (Wu et al., 5 Dec 2024); (Zhang et al., 18 Sep 2025).
• Integration with RIS/XL-MIMO: Joint optimization of FAS with large-scale intelligent surfaces or multi-antenna systems to unlock additional DoF (Wu et al., 5 Dec 2024); (Zhang et al., 26 Sep 2025).
• Security and privacy: Protection against beam hijacking and location-based attacks inherent to spatially reconfigurable apertures (Hong et al., 16 Jun 2025).

By fusing physics-based reconfigurability, advanced signal processing, and software-defined control, fluid-antenna systems offer a pathway to ultra-adaptable, high-capacity, and robust wireless architectures for the 6G era and beyond (Wu et al., 5 Dec 2024); (Lu et al., 6 Jan 2025); (Hong et al., 16 Jun 2025); (Lu et al., 6 Jan 2025); (Zhu et al., 10 Sep 2025).