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Reconfigurable Antennas for Next-generation Mobile Communication Networks: A Comprehensive Survey and Tutorial

Yizhe Zhao, Long Zhang, Halvin Yang, Kun Yang, Rui Zhang, Lingyang Song, Yuanwei Liu

arXiv:2606.12139v1cs.IT

TL;DR

Next-generation networks require antenna technologies that can adapt beyond fixed-position MIMO limitations. This paper surveys four major reconfigurable-antenna paradigms, their modelling, estimation, optimization, and integration, and concludes that each offers distinct trade-offs while substantial control, hardware, and validation challenges remain.

  • Problem

    Fixed-position MIMO systems face limited adaptability in changing channels, dense packaging and coupling issues, and weakened multipath advantages at high frequencies.

  • Method

    The paper provides a unified tutorial and survey of fluid, movable, pinching, and reconfigurable holographic antennas across modelling, estimation, performance, resource allocation, and emerging-technology integration.

  • Results

    The comparison finds that the four RA types have distinct advantages in reconfigurability, deployment complexity, and energy efficiency for different application scenarios.

  • Takeaways & Limitations

    Future RA research must address adaptive control, network-level joint optimization, and hardware trade-offs between flexibility and complexity.

  • Takeaways & Limitations

    Traditional MIMO’s fixed configurations limit adaptation to dynamic wireless environments and create coupling and efficiency problems when antennas are densely packed.

Abstract

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The transition to next-generation mobile communication networks, particularly 6G, demands advanced technologies to meet the requirements for ultra-reliable, low-latency communication, massive connectivity, and intelligent applications. Reconfigurable antennas (RAs) play a crucial role in achieving these objectives by enabling dynamic adjustments to the radio frequency (RF) characteristics of antennas, such as gain, radiation pattern, impedance, and polarization. Unlike traditional fixed-position antennas, RAs can alter both their radiation patterns and positions, offering flexibility in response to varying communication environments. This paper presents a comprehensive survey and tutorial on RAs, with a focus on fluid antennas (FAs), movable antennas (MAs), pinching antennas (PAs), and reconfigurable holographic antennas (RHAs), examining their potential in next-generation mobile networks. We explore the channel modelling and estimation, performance analysis, resource allocation strategies, and their synergy with other emerging wireless technologies for each type of RA. Finally, we provide a comparative analysis of different RAs and discuss the open challenges and future research directions, offering insights and guidance for future investigations in the exciting research area.

I. INTRODUCTION

6G’s requirements for high data rates, low latency, massive connectivity, and mobility expose limitations in fixed-position antenna systems. Reconfigurable antennas address these challenges by adapting RF characteristics, positions, or radiation patterns across several hardware paradigms.

  • A. The Need for Advanced Antenna Technologies for Next-Generation Mobile Communications: 6G targets 100 Gbps–1 Tbps peak data rates, 0.1–1 ms latency, and over 100 devices per cubic meter.These requirements create demands for wider bandwidth, rapid transmission, and highly dense device support.
  • B. Development and Limitations of Traditional MIMO Technology: Fixed-position MIMO antennas struggle with rapidly changing channels, dense packaging, mutual coupling, and reduced multipath at mmWave and THz frequencies.These constraints weaken adaptability, antenna efficiency, and some of MIMO’s spectral-efficiency advantages.
  • C. Reconfigurable Antennas: A Viable Solution: Reconfigurable antennas dynamically adjust frequency, radiation pattern, polarization, or impedance according to channel conditions or user needs.This adaptability can direct energy toward intended receivers, avoid interference, and improve robustness in complex scenarios.
  • C. Reconfigurable Antennas: A Viable Solution: RAs and IRS operate at different link locations: RAs reconfigure transceiver apertures, whereas IRS modifies propagation and can complement RA deployment.The paper distinguishes local transceiver-side spatial degrees of freedom from IRS-based coverage extension and blockage mitigation.
  • C. Reconfigurable Antennas: A Viable Solution: Fluid, movable, pinching, and holographic antennas provide distinct reconfiguration mechanisms spanning fluidic control, mechanical movement, deformation, and electromagnetic wavefront shaping.Their trade-offs include compact adaptability, mechanical complexity, response speed, reconfiguration range, and beam-shaping capability.

D. Related Surveys

The survey addresses the lack of a unified, systematic review spanning major reconfigurable-antenna paradigms. It organizes their principles, modelling, estimation, optimization, integration, and trade-offs under a common system-oriented framework.

  • Research gap: Prior tutorials and surveys largely focus on individual paradigms such as FA, MA, or RHA, using paradigm-specific assumptions and evaluation metrics.
  • Scope: The survey covers four representative RA types—FA, MA, PA, and RHA—and explains their operating principles and hardware designs.
  • Technical analysis: It reviews channel modelling, channel estimation, performance characteristics, and resource allocation under unified considerations including field applicability, mobility, overhead, and hardware nonidealities.
  • Technology integration: The survey examines integration of RAs with IRS, ISAC, semantic communication, AirComp, SWIPT, and physical-layer security.
  • Comparative framework: It compares RA types by reconfiguration domain, mechanism, latency, energy consumption, hardware cost, deployment role, and application scenario, while linking spatial DoF to operational overhead.

1) Fundamental Principle:

Fluid antennas use software-controllable fluidic or pixel-based radiating structures to reconfigure antenna characteristics and exploit spatial diversity in compact spaces. Their channel analysis must model strong port correlation, with increasingly accurate models trading analytical tractability against realism.

  • Hardware and operating principle: Fluid antennas reconfigure frequency, radiation pattern, polarization, shape, or position through software-controllable radiating structures.Liquid-based designs use conductive liquids, while pixel-based designs selectively activate interconnected metallic patches; meta-fluid antennas electronically switch programmable meta-atoms.
  • Hardware and operating principle: Liquid viscosity can limit fluid-antennas’ flow speed and response time, whereas pixel-based switching can make response time negligible.These implementation differences determine suitability for fast-changing communication environments.
  • Channel modelling: FAS channels are modeled mainly through small-scale fading because many ports occupy a small space and exhibit strong spatial correlation.The simplified 1D model represents N correlated port gains over an aperture of Wλ.
  • Channel modelling: Reference-port correlation models can overestimate FAS performance because they do not directly represent correlations between arbitrary port pairs.Common correlation parameters and fully correlated models address this limitation by describing pairwise spatial dependence more explicitly.
  • Channel modelling: Block-correlation models approximate fully correlated channels while making performance analysis more tractable than N-fold integral formulations.The approximation treats correlation as approximately constant within blocks and different blocks as independent.
  • Channel modelling: Two-dimensional planar FAS channels use 3D Clarke-based spatial correlation, with correlations expressed through a zero-order spherical Bessel or sinc function.Measurement-driven studies are also beginning to validate FA channel characteristics in practice.

2) Channel Estimation:

FAS channel estimation reconstructs channel state information from a limited subset of ports to reduce signaling overhead, exploiting spatial correlation and channel sparsity. Proposed methods range from interpolation and regression to Bayesian, learning-based, and sampling-based reconstruction, each relying on different assumptions and trade-offs.

  • Estimation motivation: Complete CSI estimation across all FAS ports is impractical, so limited-port observations are used to reconstruct the remaining channel state.The approach leverages inherent spatial correlation and sparsity.
  • Model-based methods: Skip-enabled LMMSE estimates selected ports from pilots and assigns skipped ports the CSI of their nearest estimated neighbors.This reduces signaling overhead while balancing estimation quality and complexity.
  • Model-based methods: Least-square reconstruction requires at least Lp + 1 observed ports when scattering-path count and angles are known, an assumption described as impractical.The method estimates path parameters and reconstructs the channel from them.
  • Sparse and Bayesian methods: Sparse and Bayesian methods exploit angular or spatial sparsity, but their performance can degrade when sparsity assumptions or channel conditions are violated.S-BAR addresses model mismatch with a Gaussian-process formulation, while SBL performance degrades for nonsparse channels.
  • Learning-based methods: Learning-based AGMAE predicts unobserved CSI using transformer encoding and graph-attention decoding, achieving high extrapolation accuracy with low complexity and good generalization.It requires large-scale training and may be sensitive to dynamic environments.
  • Performance analysis: FAS performance analysis includes capacity, level crossing rate, average fade duration, outage probability, and multiplexing gain under alternative port-selection and switching strategies.Fast and slow FAMA differ in whether antenna positions adjust symbol by symbol or when the channel changes.

C. Resource Allocation

FAS resource allocation adds antenna-port selection to conventional beamforming, power, and scheduling decisions. Research jointly optimizes these variables for capacity, rate, energy efficiency, interference management, sensing, security, wireless power transfer, and index modulation, often using optimization or learning-based methods.

  • Allocation framework: FAS resource allocation extends conventional decisions with an additional port-selection variable.This extra spatial choice makes allocation more flexible but also introduces high-dimensional optimization problems.
  • Beamforming and power allocation: Joint port selection, beamforming, and power allocation are used to maximize MIMO-FAS rate, capacity, energy efficiency, or multiuser sum-rate.Proposed approaches include fractional programming, zero forcing, convex relaxation, mean-field games, and low-complexity algorithms.
  • Integration with emerging technologies: FAS is integrated with ISAC, where antenna positions and dual-functional beamforming are jointly optimized for sensing SNR or communication objectives.These studies consider both perfect and imperfect CSI.
  • AI-assisted allocation: AI methods address difficult port-selection problems by learning antenna positions, predicting future CSI, or accelerating iterative optimization under mobility.Reported approaches include graph neural networks, unfolded BSUM layers, and an LLM-based port predictor.
  • Integration with emerging technologies: FAS resource allocation is also studied for secure communications, SWIPT, and index modulation, including joint beamforming-port selection and switching-delay or energy-consumption considerations.Grouping ports can mitigate spatial-correlation losses in fluid-antenna index modulation.

III. MOVABLE ANTENNA FOR NEXT-GENERATION MOBILE COMMUNICATIONS

Movable antennas (MAs) reconfigure antenna position and orientation to exploit favorable spatial channel conditions, with architectures ranging from position-adjustable arrays to six-dimensional surfaces. Their channel models extend from field-response formulations to MIMO and near-field settings, while practical implementations must balance adaptability against mechanical latency and scalability.

  • Fundamental Principle: MAs reposition or reorient antenna elements within a predefined spatial region to align with favorable channel conditions and add spatial degrees of freedom.This physical adaptability can support finer beam steering and dynamic spatial alignment.
  • MA Types: Position-adjustable MAs optimize antenna positions jointly with antenna weights, while 6DMA surfaces additionally support three-dimensional translation and rotation.6DMA provides extra spatial degrees of freedom without necessarily deploying additional antenna elements.
  • Hardware Design: A practical MA unit combines a control unit, RF link, antenna elements, and a drive structure, with actuators providing mechanical positioning or orientation adjustment.The architecture is applicable at transmitters and receivers and can use motors, gears, servo mechanisms, or MEMS.
  • Hardware Design: Mechanically driven MAs trade mature fabrication and cost advantages for response latency and maintenance constraints.MEMS mechanisms can respond in microseconds to milliseconds, whereas conventional motor-based systems typically require milliseconds to seconds.
  • 6DMA Architectures: 6DMA scalability is constrained by increasing deployment complexity and centralized CPU burden, motivating hybrid fixed–movable arrays and distributed processing.Distributed architectures assign channel estimation and precoding or detection to local processing units while the CPU optimizes surface position and rotation.
  • Channel Modelling: MA channel modelling begins with field-response models for movable transmit and receive antennas and extends to MIMO and near-field formulations.Hardware prototypes and over-the-air measurements have also been used to validate MA channel behavior.

2) Channel Estimation:

MA channel estimation must address time-varying, position-dependent channels and increased dimensionality. Existing approaches use compressed sensing, tensor decomposition, statistical 6DMA measurements, and sparse instantaneous estimation to reduce training or computational demands.

  • Challenges: MA repositioning creates time-varying, location-dependent fading and higher estimation dimensionality, limiting direct use of conventional fixed-antenna estimators.The channel response depends strongly on antenna position rather than only on angular features.
  • Compressed Sensing: Compressed sensing estimates multipath AoD, AoA, and complex gains, but its O(L · M · G) complexity and grid dependence make high-resolution estimation costly.Accuracy also depends on the measurement positions of the transmit and receive MAs, and the framework mainly considers SISO systems.
  • Tensor Decomposition: Tensor decomposition arranges pilots from successive transmitter and receiver antenna movements into a third-order tensor for channel estimation and reconstruction.Canonical polyadic decomposition is then used to extract factor matrices.
  • 6DMA Estimation: Statistical 6DMA estimation exploits directional sparsity because substantial gains occur only for a subset of position–rotation pairs.A three-stage procedure collects limited measurements, estimates multipath parameters, and uses them for statistical optimization.
  • 6DMA Estimation: Pilot length is reduced by about 50% through sparse instantaneous 6DMA estimation, which improves accuracy over traditional LS while having slightly higher NMSE than exhaustive covariance-based measurement.The method focuses on valid channel regions and has complexity O(L2KMg + MaG).

3) Performance Analysis:

Performance analyses show that MAs improve communication and sensing by exploiting position and orientation reconfiguration for array gain, multiplexing, interference suppression, and geometry. The section also links these gains to resource-allocation methods that jointly optimize antenna configuration with beamforming, power, or positioning decisions.

  • Theoretical Performance: MA theory characterizes channel gain, maximum movement-region gain, average SNR improvement, and outage probability across LoS and multipath conditions.Broadband OFDM studies further show that MA localization can synthesize desired impulse responses with maximum gain and tunable phase.
  • Performance Gains: 6DMA dynamically matches antenna configurations to user locations, providing array gain, spatial multiplexing, interference suppression, and geometric sensing gain over fixed antennas.These gains arise from jointly adapting antenna positions and orientations to communication or sensing conditions.
  • Array Gain: The channel capacity of bilateral XL-MIMO with ROMA is about 1.4 times higher than with an FPA.This result illustrates the array-gain benefit of steering antenna positions and orientations toward desired directions.
  • Spatial Multiplexing Gain: Network capacity increases by 60%, 305%, and 656% over FAR, CAM, and FPA baselines, respectively.The reported gains are associated with positioning and rotation that shape the MIMO channel singular-value profile.
  • Interference Suppression: 6DMA achieves about 15 dB SINR gain when the number of cochannel users is 12 by enhancing desired-signal power and suppressing interference.Long-term channel-aware placement and orientation can complement instantaneous CSI for beamforming.
  • Resource Allocation: Resource allocation jointly considers antenna positioning, movement trajectories, beamforming, and power control, using methods such as two-timescale optimization, reinforcement learning, AO, SCA, and BCD.These strategies address the additional spatial degrees of freedom introduced by MA reconfiguration.

D. Convergence with Other Wireless Technologies

MAs are being combined with sensing, security, UAV, MEC, and AirComp systems to jointly optimize spatial configuration with communication, computation, or sensing objectives. These integrations extend MA adaptability across dynamic platforms and multi-function wireless systems, while pinching antennas are treated as a related reconfigurable-antenna technology.

  • ISAC: MA-assisted ISAC jointly optimizes antenna positioning, beamforming, and signal design to improve communication and sensing performance.One studied system uses a movable array on a base station in a bistatic radar-enabled multi-user MISO framework.
  • Physical-Layer Security: Physical-layer-security studies combine movable antennas with iterative optimization to improve secrecy-related objectives against eavesdroppers or monitoring systems.The considered designs include linearly movable transmit antennas and MA-equipped jammers.
  • UAV Integration: MA-equipped UAV systems jointly configure antenna positions with UAV trajectory, three-dimensional pose, beamforming, or base-station selection for adaptive wireless links.Antenna movement supplies an additional fine-grained spatial degree of freedom in dynamic environments.
  • MEC Integration: MA–MEC integration couples spatial antenna control with edge computation, storage, and caching to address computation-intensive or delay-sensitive applications.The combined systems target user experience, wireless link quality, and end-to-end latency.
  • AirComp Integration: MA-enabled AirComp jointly optimizes transmit power, antenna positions, and receive combining to minimize computation mean-squared error.Alternating optimization is used in the reported framework, with extensions to 2DMA arrays at access points.
  • Pinching Antennas: Pinching antenna systems are surveyed separately with emphasis on their operating principle, channel modelling and estimation, and resource allocation.They are presented as another reconfigurable-antenna approach alongside MA-based integrations.

A. Basic Principle of PA

Pinching antennas (PAs) use dielectric units along waveguides to dynamically control electromagnetic propagation and support flexible radiation. Their models capture single- and multi-antenna signal formation, while power-allocation choices trade analytical insight against hardware simplicity.

  • Basic principle: Pinching antennas place dielectric particles along dielectric waveguides to dynamically manipulate electromagnetic propagation.The concept was experimentally validated by DOCOMO in 2021.
  • Basic principle: A typical PA system combines a base station, dielectric waveguide, and discrete dielectric pinch antennas.The base station processes baseband signals, while the waveguide distributes RF signals.
  • Signal modelling: PA signal models treat each antenna as an open-ended directional waveguide coupler with adjustable radiation and negligible reflection.The analytical assumption is ideal full radiation from the open end.
  • Signal modelling: For multiple PAs, the received signal is the superposition of individual contributions, while each antenna’s radiated power depends on preceding power extraction.The antenna distance r_m determines each contribution, and the model accounts for sequential coupling along the waveguide.
  • Power allocation: The Equal Power Model supports performance analysis but may require different antenna lengths, whereas the Proportional Power Model uses identical antennas to reduce hardware costs.The two models represent different trade-offs between analytical flexibility and implementation simplicity.
  • Validation: Publicly available measurement campaigns and open datasets for directly validating PA channel models remain absent.Existing evaluations are therefore mainly analytical and simulation-based because repeatable measurement requires calibrated waveguide pinching and controllable radiating points.

2) Channel Estimation:

PASS channel estimation must acquire spatially flexible channel state information while controlling pilot overhead and computational complexity. Existing approaches use physical channel priors or data-driven inference, with representative learning-based and sparse parametric methods addressing scalability and efficiency.

  • Channel-estimation challenges: PASS enables antenna activation at arbitrary, potentially continuous, waveguide positions, creating new challenges for efficient CSI acquisition.Unlike fixed arrays, channel information cannot simply be acquired independently for a fixed set of elements.
  • Estimation approaches: Existing PASS channel-estimation strategies fall into model-based parametric methods and learning-based methods.Parametric methods exploit sparse multipath structure, particularly in mmWave scenarios, while learning-based methods use neural networks.
  • Learning-based estimation: PAMoE uses a mixture-of-experts architecture to process PA positions and pilot signals for high-accuracy estimation.Expert specialization is the central mechanism described for its estimation performance.
  • Learning-based estimation: PAformer uses self-attention to accommodate different numbers of PAs at test time, including dimensionalities not seen during training.This provides a zero-shot learning capability and addresses PAMoE’s predefined maximum-PA constraint.
  • Parametric estimation: Conventional LS estimation activates all candidate antennas, whereas sparse parametric estimation activates a limited subset to emulate a large aperture with lower switching and pilot overhead.PASS near-field operation also requires distance-dependent channel models.
  • Performance context: PASS performance studies report gains in rate, reliability, and flexibility while considering blockage, waveguide loss, and mutual coupling.The surveyed results characterize PASS as resilient under these practical impairments.

2) Placement Design and Power Allocation:

PASS placement and power-allocation research jointly exploits antenna positions, waveguide assignment, beamforming, and transmit power across diverse communication and sensing systems. Proposed methods address rate, energy efficiency, interference management, sensing, wireless power transfer, security, and aggregation accuracy.

  • Placement and power allocation: Single- and multi-user systems optimize antenna positions with relaxed optimization, particle swarm optimization, matching models, bisection, and graph-based learning.The objectives include rate maximization, QoS guarantees, sum-rate, and energy efficiency across OMA, NOMA, and multicast settings.
  • Placement and power allocation: Multi-waveguide PASS power allocation achieved up to 76% power reduction while revealing oscillatory behavior linked to waveguide separation.Other formulations jointly optimize waveguide assignment, antenna activation, SIC order, and power allocation.
  • Emerging applications: PASS resource optimization extends to uplink transmission, integrated sensing and communications, wireless information and power transfer, secrecy, covert communication, artificial noise, and AirComp.These studies optimize combinations of placement, power, beamforming, sensing, security, or aggregation variables.
  • RHA principle: RHA beamforming uses resonance-circuit tuning rather than phase shifters, with three beamforming models defined through element coefficients.Its half-wavelength element spacing supports high element density and compact analog beamforming.

2) Hardware Design:

RHA hardware combines dense metamaterial elements, tunable resonators, layered PCB construction, and electronically controlled biasing. Its physical architecture imposes a leakage-power constraint and motivates channel models ranging from LoS-focused parametric formulations to electromagnetic and wavenumber-domain descriptions, alongside measurement validation.

  • Hardware design: RHA systems predominantly use PCB fabrication with PIN or varactor diodes embedded in cELC resonators to modulate element radiation.Electronic bias control dynamically adjusts the metamaterial elements.
  • Hardware design: The five-layer RHA hardware includes cELC resonators, an RF substrate with waveguide, a ground plane, a spacer dielectric, and a DC bias-feed layer.Control vias deliver bias voltage to tune cELC resonance and thereby transmitted-signal amplitude and phase.
  • Physical constraint: RHA beamforming must obey energy conservation because reference-wave power decays through radiation leakage along the series-fed waveguide.Total radiated power cannot exceed input power.
  • Channel modelling: Dense RHA elements create higher spatial correlation than conventional half-wavelength phased arrays, requiring distinct channel models.The surveyed models include parametric physical, electromagnetic Green’s-function, and wavenumber-domain formulations.
  • Channel modelling: The parametric physical model suits strong-LoS environments, whereas electromagnetic and wavenumber-domain models provide broader physical or analytical representations.The LoS-focused model may degrade when NLoS components dominate, while electromagnetic formulations are difficult to apply in practice.
  • Experimental validation: A 2.6 GHz EC-VDA measurement campaign used a fabricated receiver prototype in a 10 m line-of-sight link to provide empirical RHA evidence.The setup paired a conventional horn transmitter with the electrically controlled virtual dense array receiver.

2) Channel Estimation:

RHA channel estimation spans linear, sparse, AI-based, Gaussian-process, and tensor methods, alongside analyses of capacity, DoF, latency, energy efficiency, and computational complexity. These studies address high-dimensional CSI acquisition while exposing scalability limits in large-scale systems.

  • Channel estimation methods: Linear estimation is easy to implement without prior receiver statistics but requires many RF chains and costly matrix inversion or eigenvector decomposition at scale.These limitations degrade performance in large-scale RHA systems.
  • Channel estimation methods: RHA channel estimation methods include linear estimators, sparse reconstruction, AI-based learning, Gaussian-process regression, and tensor-based recovery.The methods target large-scale, dynamic, sparse, or physically consistent channel models.
  • Performance analysis: Continuous-aperture RHA provides a theoretical upper bound for discrete arrays through electromagnetic field-based capacity analysis.The framework derives closed-form channel-capacity expressions and extends analysis to Rician fading and inter-receiver interference.
  • Performance analysis: RHA prototypes achieve circuit-level beam switching within 2 µs and system-level switching within 50 µs.FPGA-based implementations have demonstrated switching times as low as 3 µs.
  • Performance analysis: A 256-element RHS consumes approximately 10 W, saving 56.52% power compared with a 23 W phased array offering equivalent gain.Individual PIN diodes consume around 0.01 W.
  • Performance analysis: Hierarchical scale-changeable-aperture codebooks reduce beam-training overhead to O(log N) compared with exhaustive search.The design targets the computational burden of high-dimensional channel estimation.

C. Resource Allocation

RHA resource allocation emphasizes beamforming, aperture selection, and AI-assisted optimization under practical hardware and propagation constraints. These strategies support communication, satellite, sensing, and wireless-power applications while targeting efficiency and reduced complexity.

  • Beamforming design: Hybrid beamforming jointly optimizes digital base-station processing and holographic surface beamforming to maximize multi-user sum-rate.The design accounts for spherical-wave propagation and dual-wideband effects under practical hardware constraints.
  • Beamforming design: HDMA simplifies multi-user holographic beamforming by superimposing known holographic patterns.The scheme addresses the high computational complexity caused by the large number of RHA elements.
  • Beamforming design: AI-powered holographic beamforming learns mappings from far-field electric fields to excitation coefficients for high-precision superdirective beamforming.The approach is designed to adapt to hardware imperfections, environmental dynamics, and multi-objective tasks.
  • Hardware selection: Activating only selected portions of a continuous aperture can improve uplink SNR and provide selection diversity under LoS and NLoS channels.Nearest-neighbor and segmented aperture-selection strategies reduce active-aperture complexity.
  • Satellite communications: RHA beamforming supports LEO satellite communication through sum-rate optimization, real-valued amplitude constraints, and satellite tracking.Reported benefits include directive gain, low power consumption, cost efficiency, and compact user terminals.
  • WPT and SWIPT: In near-field SWIPT, joint digital precoding and holographic beamforming maximize sum-rate under energy-harvesting constraints.The reported system outperforms conventional phased arrays in spectral efficiency and power-transfer efficiency.

4) Cell Free:

The comparison presents FA, MA, PA, and RHA as complementary reconfigurable-antenna paradigms with different physical mechanisms, spatial DoF, latency, hardware, and CSI-overhead trade-offs. RHA is also positioned for compact distributed deployments, while outage performance improves with larger motion ranges, more PA elements, or larger RHA apertures.

  • Cell-free deployment: RHA offers lower cost, compact form factor, simpler control circuitry, and local-CSI beamforming for cell-free large-scale wireless networks.The cited study reports robustness under near-field conditions and hardware impairments.
  • Overall comparison: FA, MA, PA, and RHA reconfigure different physical properties, producing distinct trade-offs in spatial DoF, latency, hardware complexity, and CSI overhead.FA selects ports or positions, MA changes geometry, PA slides activation points along waveguides, and RHA controls aperture fields.
  • Flexibility: FA switching can reach the microsecond regime, whereas liquid FA depends on fluid motion and MA is constrained by mechanical actuation.These mechanisms create different flexibility and mobility profiles across paradigms.
  • Channel estimation: Channel-estimation complexity is dominated by end-to-end CSI acquisition, including training, scanning, selection, calibration, and feedback.The burden scales with the reconfiguration degrees of freedom and differs across FA, MA, PA, and RHA.
  • System integration: RA paradigms commonly complement conventional MIMO beamforming and scheduling rather than fully replacing fixed antennas.The preferred choice depends on latency, overhead, DoF, and deployment role.
  • Outage probability: Increasing FA motion range W, MA search area, PA antenna count M, or RHA array size improves outage performance in the considered transmitter-side RA scenario.FA and MA use one active element, PA dynamically places M antennas along a waveguide, and RHA electronically reconfigures an M×M surface.

D. Security Vulnerabilities of Different RAs

Reconfigurable antennas introduce security risks through configuration signaling, CSI acquisition, and cyber-physical actuation. The survey also identifies broader adoption barriers involving adaptive control, cross-layer integration, standardization, experimental validation, and sustainability.

  • FA security: FA vulnerabilities include tampered switching logic, biased CSI-based port selection, and side-channel leakage of active antenna states.The authors recommend authenticated configuration signaling, robust CSI validation, and fallback states for abnormal switching.
  • MA security: MA cyber-physical loops can suffer over-actuation, degraded sparse recovery, and adversarially misled position or pose optimization.These effects may accelerate mechanical wear and, in hard-to-maintain base-station deployments, contribute to persistent or permanent denial of service.
  • PA security: PA systems face interception and privacy risks because predictable line-of-sight paths, CSI-dependent activation, and side-channel signatures can expose antenna states or beam directions.Pilot contamination, jamming, and CSI poisoning can also cause activation errors and beamforming problems.
  • RHA security: RHA excitation interfaces controlled by FPGA or microcontroller circuits can be hijacked to distort wavefronts, silence transmission, or steer energy toward unintended directions.The controllable excitation interface is therefore a direct attack surface for active radiating apertures.
  • Unified security requirements: RA-enabled systems should protect configuration signaling and CSI acquisition while monitoring abnormal reconfiguration, especially where control interfaces or cyber-physical actuation are dense.This unified security requirement spans the vulnerabilities identified across FA, MA, PA, and RHA designs.
  • Open challenges: Open adoption barriers include static control logic, predominantly physical-layer optimization, absent unified protocols and metrics, limited experimental validation, and underexamined sustainability.The survey calls for AI-enabled control, cross-layer co-design, standardization, hardware-in-the-loop validation, measurement campaigns, and greater attention to lifecycle impacts.
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