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Movable-Element STAR-RIS for Integrated Sensing and Communication: Architectures, Opportunities, and Practical Challenges

Wali Ullah Khan, Muhammad Adil

arXiv:2610.00145v1cs.ET

TL;DR

Fixed STAR-RIS elements cannot adapt the surface geometry to users, sensing targets, blockage, or near-field focusing conditions. This article develops a system-level ME–STAR–RIS ISAC perspective that jointly controls electromagnetic response and element positions. In an illustrative study, mobility improves the sampled communication–sensing tradeoff, while rate gains rapidly saturate with modest travel.

  • Problem

    Fixed STAR-RIS element locations leave aperture geometry unable to adapt to users, sensing targets, blockage, or near-field focusing conditions.

    Source: §I · p. 1

  • Method

    The article develops a system-level ME–STAR–RIS ISAC framework that jointly considers electromagnetic response and geometric reconfiguration.

    Source: §I · pp. 1–2

  • Results

    At illumination weight 0.05, the fixed surface provides 3.19 bit/s/Hz and −123.22 dB mean normalized illumination power, whereas ME–STAR–RIS provides 5.42 bit/s/Hz and −122.79 dB.

    Source: §VII · p. 8

  • Takeaways & Limitations

    D = 0.5λ captures about 94% of the rate gain available at D = 1λ, supporting evaluation of mobility gain against movement cost with fixed STAR-RIS as the benchmark.

    Source: §VII · p. 8, §IX · p. 9

    In a single-user far-field line-of-sight link with ideal continuous and independently adjustable phases, a fixed surface can leave little extra rate gain for position control.

    Source: A. Communication gain through channel… · p. 4

Abstract

from arXiv · show

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) extend conventional reflecting-only surfaces by enabling controllable full-space propagation. Yet, once deployed, the physical locations of their elements remain fixed, leaving the surface geometry unable to adapt to users, sensing targets, blockage, or near-field focusing conditions. This article develops a system-level perspective on movable-element STAR-RIS (ME--STAR--RIS) for integrated sensing and communication (ISAC), where the surface jointly reconfigures its electromagnetic response and the physical positions of its elements. We explain how geometric reconfiguration can reshape the effective aperture, spatial correlation, interference nulls, and sensing illumination while preserving STAR-RIS full-space operation. A two-timescale control architecture separates relatively slow element motion from fast beamforming and transmission/reflection control. A 100-realization illustrative case study compares ME--STAR--RIS with an otherwise identical fixed STAR-RIS under a passive coupled transmission/reflection response. The results show a substantially improved sampled communication--sensing tradeoff and, importantly, a rapid saturation of the rate gain with modest element travel. We conclude with representative use cases, implementation constraints, and a research roadmap covering mobility overhead, channel acquisition, mutual coupling, near-field operation, hardware impairments, and learning-assisted predictive control.

I. INTRODUCTION

STAR-RIS enables full-space service, but fixed element geometry limits how the surface can adapt to users, targets, and propagation conditions. This article frames movable-element STAR-RIS as a system-level ISAC architecture, combining geometric adaptation with STAR response control and examining its operation, applications, evaluation, and practical constraints.

I. INTRODUCTION

  • ISAC and RIS are presented as complementary because shared resources support communication and sensing while RIS makes propagation partially programmable.The introduction situates these technologies as motivation for the proposed system perspective.Source: §I · p. 1
  • Fixed STAR-RIS geometry leaves aperture, spacing, sampling pattern, and location-dependent channel phases predetermined, despite controllable transmission and reflection responses.This can restrict distinct user and target spatial signatures, focusing conditions, and interference-null locations.Source: §I · p. 1
  • Prior work includes movable antennas, fluid RIS, and STAR-RIS with movable elements for sensing and covert communication, so the gap is a broader ISAC design perspective.The article focuses on when geometry helps, how to control it, and how to evaluate its communication–sensing benefits under hardware constraints.Source: §I · p. 1
  • The article develops a system-level framework spanning architecture, operation, control, deployment opportunities, performance evaluation, and implementation challenges.It distinguishes wavefront, STAR transmission/reflection, and geometric reconfiguration, then considers two-timescale control and a 100-realization case study.Source: §I · p. 2

II. FROM PROGRAMMABLE WAVES TO PROGRAMMABLE GEOMETRY

  • ME–STAR–RIS jointly controls electromagnetic response and physical element geometry, adding a spatial degree of freedom to fixed-aperture STAR-RIS optimization.The article considers propagation regimes where mobility is beneficial and a hardware-aware framework for assessing communication–sensing benefits.Source: §I · pp. 1–2, §II · p. 2

A. Three layers of reconfigurability

ME–STAR–RIS combines three forms of reconfigurability: wavefront control, STAR transmission/reflection functionality, and movable element geometry. Together, these let the surface shape fields and adapt its aperture for communication and sensing objectives.

A. Three layers of reconfigurability

  • Movable geometry adds a third reconfigurability layer to wavefront and STAR functional control, enabling adaptive aperture geometry for ISAC.Fig. 2 depicts the three layers as amplitude/phase, transmission/reflection, and movable-position control.Source: A. Three layers of reconfigurability · p. 2
  • Wavefront reconfiguration changes the reradiated field’s amplitude and phase to steer energy, suppress interference, or synthesize a spatial response.This is the classic RIS function.Source: A. Three layers of reconfigurability · p. 2
  • STAR functional reconfiguration controls whether and how incident energy is transmitted and reflected, but practical passive responses may be coupled.Energy splitting, mode switching, and time switching can serve nodes on both sides; coupled responses mean the surface is not two independent colocated RISs,.Source: A. Three layers of reconfigurability · p. 2
  • Geometric reconfiguration moves elements within prescribed regions or tracks, changing propagation distances, spatial phases, and sometimes path amplitudes.Geometry changes the channel state on which faster electronic control operates; in ISAC it also makes the aperture a resource for competing communication and sensing objectives.Source: A. Three layers of reconfigurability · p. 2

B. How is ME–STAR–RIS different from neighboring concepts?

ME–STAR–RIS differs from movable-antenna and movable-RIS designs by moving the surface elements while retaining STAR transmission and reflection. Its shared aperture supports full-space ISAC, with practical operation constrained by coupled responses and simultaneous effects on both sides.

B. How is ME–STAR–RIS different from neighboring concepts?

  • Unlike movable-antenna-assisted RIS, which moves a transceiver while the surface stays fixed, ME–STAR–RIS changes surface geometry and several cascaded links.Movable/fluid RIS designs typically focus on reflection; ME–STAR–RIS combines movable geometry with simultaneous transmission and reflection.Source: B. How is ME–STAR–RIS different from… · pp. 2–3
  • ME–STAR–RIS uses fast electronic coefficients to shape transmitted and reflected fields and slower position control to reshape the aperture and channel geometry.This distinction matters when users and sensing targets lie on opposite sides or impose competing spatial requirements.Source: B. How is ME–STAR–RIS different from… · p. 3

III. ME–STAR–RIS-ENABLED ISAC ARCHITECTURE

  • The architecture must account for STAR operation, links on both sides, practical hardware coupling, and position changes that affect several cascaded paths.The article describes a multi-antenna BS with monostatic sensing; in the illustrated transmission-side case, the target echo returns through the surface to the same BS.Source: §III · p. 3, A. Full-space ISAC operation and… · p. 3

A. Full-space ISAC operation and hardware constraints

  • Each element has a feasible movement region or track, transmission/reflection controls, and a minimum-separation rule; movement may use mechanical, fluidic, MEMS, pixel-based, or subarray implementations.For passive energy-splitting STAR hardware, energy conservation and coupled transmission/reflection responses remain active during position optimization.Source: A. Full-space ISAC operation and… · pp. 3–4
  • The shared movable aperture can serve users and sensing targets in either half-space, while STAR coefficients allocate and shape energy across both regions.A position favoring one side can alter channels on the other side as well.Source: B. How is ME–STAR–RIS different from… · p. 3, A. Full-space ISAC operation and… · p. 4

B. Position-dependent propagation

Element motion changes the spatial channels used by communication and sensing, not merely an isolated steering-vector entry. Its effects vary by propagation regime, including phase changes in far-field line of sight and phase, amplitude, and focusing changes in the near field.

B. Position-dependent propagation

  • A STAR-element position update changes both the incident BS-to-surface path and outgoing paths to users or targets, altering multiple cascaded channels at once.This position dependence makes optimization harder than for a single movable antenna.Source: B. Position-dependent propagation · p. 4
  • In far-field line-of-sight conditions, small movements mainly create phase perturbations determined jointly by incident and outgoing directions.In multipath channels, movement can also select spatial fading peaks and alter channel correlation.Source: B. Position-dependent propagation · p. 4

IV. WHAT DOES ELEMENT MOBILITY BRING TO ISAC?

  • In the radiative near field, movement changes phase and distance-dependent amplitude, allowing control of focusing depth, focal location, and effective Fresnel aperture.The benefits arise through changes to the spatial channel seen by communication and sensing functions.Source: B. Position-dependent propagation · p. 4, §IV · p. 4

A. Communication gain through channel shaping

ME–STAR–RIS adds geometry control to STAR coefficient control, allowing the surface to reshape communication channels and adapt sensing aperture geometry. The article frames this as a way to improve communication–sensing tradeoffs under shared objectives.

A. Communication gain through channel shaping

  • Element motion changes spatial sampling, helping reduce destructive multipath, reshape inter-user correlation, and suppress interference beyond phase-only nulling.A fixed surface can compensate channel phases but cannot change its spatial sampling pattern.Source: A. Communication gain through channel… · p. 4
  • In single-user far-field line-of-sight links with ideal continuous independent phases, fixed-surface alignment can leave little additional rate gain for position control.The case for mobility is stronger with conflicting spatial requirements, coupled phases, multipath, near-field operation, or quantized hardware.Source: A. Communication gain through channel… · p. 4

B. Sensing gain through adaptive aperture geometry

  • Adaptive element positions can match the virtual aperture to a sensing sector, improving angular discrimination or near-field focusing through changed propagation distances.Nonuniform sampling can also control sidelobes or avoid ambiguous spatial patterns.Source: B. Sensing gain through adaptive… · p. 4

C. Geometry-assisted communication–sensing tradeoff

  • Repositioning the surface adds a geometry resource that can let the same fast-domain coefficients satisfy communication and sensing objectives more efficiently.The proposed comparison varies target-illumination emphasis under identical hardware and power budgets, interpreting the result as a sampled tradeoff rather than a globally optimal frontier.Source: C. Geometry-assisted… · p. 4

V. TWO-TIMESCALE DESIGN AND CONTROL FRAMEWORK

The control framework separates slow physical geometry changes from faster electronic ISAC adaptation. Feedback between the layers balances communication and sensing benefits against movement, calibration, and computation costs.

V. TWO-TIMESCALE DESIGN AND CONTROL FRAMEWORK

  • The two-timescale framework holds geometry fixed after movement, settling, and calibration while fast electronic controls respond to short-term channel conditions.Physical motion incurs actuation, settling, and calibration overhead, unlike faster beamforming and STAR control.Source: §V · pp. 4–5

A. Slow-timescale geometry adaptation

  • The slow controller uses persistent spatial information to reposition elements or subarrays toward stronger links, lower user correlation, better target illumination, or balanced ISAC utility.Updates can be periodic or event-driven and must respect movement regions, spacing, energy, settling-time, and positioning-accuracy constraints.Source: A. Slow-timescale geometry adaptation · p. 5

B. Fast-timescale electronic ISAC control

  • After geometry settles, the fast controller updates communication and sensing beamformers, power, scheduling, and STAR coefficients under practical transmission/reflection constraints.Existing STAR-RIS ISAC methods can operate within each geometry state instead of triggering physical movement whenever instantaneous CSI changes.Source: B. Fast-timescale electronic ISAC… · p. 5

C. Cross-timescale coordination and complexity

  • Feedback lets the slow controller weigh movement against expected gain because a communication-favorable geometry can weaken illumination, while a sensing-oriented aperture can increase user correlation.Geometry optimization adds position variables and constraints to nonconvex design; codebooks or subarray motion can reduce complexity, and quasi-static gains do not imply symbol-level movement.Source: C. Cross-timescale coordination and… · p. 5

VI. APPLICATION AND DEPLOYMENT OPPORTUNITIES FOR ME–STAR–RIS-ENABLED ISAC

The proposed deployment opportunities center on settings where users or targets occupy both sides of a surface or where spatial conditions change over time. Slow geometry adaptation can complement faster STAR control in roadside and indoor ISAC.

VI. APPLICATION AND DEPLOYMENT OPPORTUNITIES FOR ME–STAR–RIS-ENABLED ISAC

  • ME–STAR–RIS may be particularly valuable when spatial conditions change, users and targets lie on both sides, or fixed geometry limits simultaneous communication and sensing.Slow geometry adaptation can reshape effective aperture, propagation paths, and coverage for prevailing deployment conditions.Source: §VI · p. 5

A. Vehicular and roadside ISAC

  • Roadside surfaces can adapt geometry to traffic flow and target sectors while STAR coefficients track moving vehicles, serving users in front of and behind the surface.Element mobility could tailor the aperture to dominant angles or compensate long-term vehicle geometry and blockage changes.Source: A. Vehicular and roadside ISAC · p. 5

B. Indoor smart environments

  • Indoor layouts place users and targets on both sides of surfaces and change more slowly than wireless fading, fitting slow geometry updates with fast electronic control.Potential indoor functions include connectivity, localization, occupancy sensing, gesture recognition, and environment mapping.Source: B. Indoor smart environments · p. 5

C. UAV and aerial networks

Aerial, near-field, and non-terrestrial settings present changing angles, focusing needs, or service geometry that may benefit from element or subarray repositioning. The practical value depends on tracking spatial changes without excessive movement or reliability costs.

C. UAV and aerial networks

  • For aerial links with strong line-of-sight components and changing angles, modest geometry changes may create predictable phase shifts that complement STAR control.Subarray movement or electronically emulated position changes may be more practical than independent mechanical motion for every element.Source: C. UAV and aerial networks · p. 5

D. Near-field and extremely large surfaces

  • In the radiative near field, movable geometry can adjust spatial sampling density and path-length distribution for focusing at multiple communication and sensing points.Movement-region size, movement matching, and cost are central design variables for large apertures.Source: D. Near-field and extremely large… · p. 6

E. Non-terrestrial and integrated networks

  • Satellite, HAPS, UAV, and terrestrial layers vary in incident angles and service geometry, making robust positioning, vibration tolerance, calibration, and actuation reliability relevant design concerns.Slow geometry updates can track persistent spatial changes while electronic coefficients respond to fast channel variation.Source: E. Non-terrestrial and integrated… · p. 6
  • The value of geometric reconfiguration depends on propagation geometry, mobility, blockage, and competing communication and sensing requirements.The article transitions from these qualitative opportunities to a controlled comparison against an otherwise identical fixed STAR-RIS.Source: E. Non-terrestrial and integrated… · p. 6

VII. ILLUSTRATIVE CASE STUDY: QUANTIFYING THE MOBILITY GAIN

In a 100-realization local benchmark, ME–STAR–RIS improves communication and target illumination over fixed STAR-RIS, with rate gains saturating at modest travel. The case study’s shared coupled response and practical deployment challenges qualify these findings.

VII. ILLUSTRATIVE CASE STUDY: QUANTIFYING THE MOBILITY GAIN

  • The fixed and movable surfaces share element counts, nominal half-wavelength grids, channel realizations, transmit power, and fast-domain beamforming, with direct BS–user and BS–target paths blocked.ME–STAR–RIS adds out-of-plane motion along short tracks normal to the surface, preserving half-wavelength tangential spacing.Source: §VII · p. 7
  • Both architectures receive 320 phase-gradient updates, but only ME–STAR–RIS jointly refines phase and position; the result is a reproducible local benchmark, not a global-optimality claim.Fast control uses zero-forcing data beams, a sensing beam in the user-channel null space, and an 80%/20% data/sensing power split.Source: §VII · p. 7
  • At 35 dBm with 64 elements, average sum rate rises from 3.19 to 5.42 bit/s/Hz, while mean normalized target illumination improves by 0.44 dB at 64 elements and 0.68 dB at 128.The comparison uses 100 realizations and a shared coupled STAR response for transmission- and reflection-side users.Source: §VII · p. 8
  • At D = 0.5λ, average rate reaches 5.42 bit/s/Hz versus 5.58 bit/s/Hz at D = 1λ, capturing about 94% of the 1λ rate gain with 0.13λ average absolute displacement.Across the sampled overlapping illumination range, ME–STAR–RIS maintains a higher communication rate; panel (c) does not claim a global Pareto frontier.Source: §VII · p. 8

VIII. PRACTICAL CHALLENGES AND RESEARCH ROADMAP

  • Practical deployment must resolve hardware-, channel-, and control-level challenges before theoretical mobility gains can survive actuation overhead, imperfect environmental knowledge, electromagnetic interactions, and real-time constraints.Source: §VIII · p. 8

A. Movement speed, energy, and reliability

Practical ME–STAR–RIS control must weigh the benefits of movement against its costs and account for channel changes, position uncertainty, and dynamic coupling.

A. Movement speed, energy, and reliability

  • Actuation consumes energy, adds delay, and may reduce hardware lifetime, motivating net ISAC utility rather than raw rate or sensing performance as the movement objective.Subarray/group movement, sparse movable elements, or event-triggered repositioning may capture most gain at lower complexity.Source: A. Movement speed, energy, and… · p. 8

B. Channel acquisition after geometry changes

  • Geometry updates change BS–surface and surface–user/target channels, so geometry-aware parametric models and sensing-derived geometry can help predict candidate-displacement channel changes.Re-estimating every cascaded coefficient from scratch after each update could negate much of the benefit.Source: B. Channel acquisition after geometry… · p. 8

C. Positioning errors and calibration

  • Mechanical tolerances and sensor errors translate into phase errors, particularly at high carrier frequencies, so robust designs should model position uncertainty and calibrate commanded coordinates to electromagnetic response.Local coupling and substrate conditions may affect the element response.Source: C. Positioning errors and calibration · p. 8

D. Dynamic mutual coupling

  • Mobility changes inter-element spacing and mutual coupling; ignoring coupling can yield unrealistic clustered geometries, while coupling-aware design may exploit collective response.Electromagnetic-compliant channel and surface models are needed to assess how much theoretical gain survives in hardware.Source: D. Dynamic mutual coupling · p. 8

E. Near-field and wideband effects

The roadmap calls for wideband and near-field modeling, predictive movement, and disciplined benchmarking. The conclusion frames mobility as a scenario-dependent, slower geometric layer complementary to fast STAR coefficient optimization.

E. Near-field and wideband effects

  • At large apertures, models must capture spherical-wave propagation and frequency-dependent focusing; single-center-frequency position optimization may cause beam squint across wide bandwidth.Future geometry design should balance multiple frequencies, users, and sensing ranges rather than optimize a narrowband steering vector.Source: E. Near-field and wideband effects · p. 8

F. Learning-assisted predictive movement

  • Predictive movement can use learned mobility patterns, road geometry, occupancy, or satellite/UAV trajectories to reposition before channel deterioration, while fast beamforming remains model-based.Learning is especially attractive for infrequent, combinatorial decisions at the slow timescale.Source: F. Learning-assisted predictive movement · p. 8

G. Standardized benchmarking

  • Benchmark reports should specify movement region and model, minimum spacing, fixed-baseline footprint and element count, hardware constraints, movement cost, CSI assumptions, and near-field or coupling treatment.Without these details, mobility gains can be difficult to interpret.Source: G. Standardized benchmarking · p. 8

IX. CONCLUSION

  • ME–STAR–RIS is a slower complementary layer to STAR coefficient optimization, with scenario-dependent value when spatial objectives compete or hardware and propagation leave geometric limitations.The conclusion calls for hardware-aware, two-timescale design that evaluates mobility gain against movement cost and uses fixed STAR-RIS as the primary benchmark.Source: §IX · p. 9
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