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arxiv: 2605.08852 · v1 · submitted 2026-05-09 · 📡 eess.SP

Recognition: no theorem link

Holographic Surface Enabled Integrated Sensing and Communications

Haobo Zhang , Shuhao Zeng , Xinyuan Hu , Shupei Zhang , Boya Di , Hongliang Zhang , Lianlin Li , Lin Chang
show 7 more authors
Weixiang Jiang Shaohui Sun Zhu Han Yonina Eldar Rui Zhang M\'erouane Debbah Lingyang Song
Authors on Pith no claims yet

Pith reviewed 2026-05-12 01:01 UTC · model grok-4.3

classification 📡 eess.SP
keywords reconfigurable holographic surfacesintegrated sensing and communicationsholographic beamformingultra-massive MIMOleaky-wave antennas6G networksjoint optimization
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The pith

Reconfigurable holographic surfaces enable cost-efficient ultra-massive MIMO for joint sensing and communications.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This tutorial introduces holographic integrated sensing and communications (HISAC) as a way to scale integrated sensing and communications to ultra-massive MIMO without the prohibitive cost and power of conventional phased arrays. It shows how reconfigurable holographic surfaces, acting as leaky-wave antennas, support holographic beamforming that jointly handles data transmission and environmental sensing while respecting a leakage power constraint. The paper develops a general optimization framework, demonstrates gains in joint, sensing-assisted, and communication-assisted modes, and reports system implementations with experimental results to illustrate practical feasibility under hardware limits.

Core claim

HISAC employs reconfigurable holographic surfaces (RHSs), a type of leaky-wave antenna, as a cost- and energy-efficient implementation of ultra-massive MIMO-based ISAC, and offers enhanced flexibility for ISAC beam synthesis through holographic beamforming.

What carries the argument

Reconfigurable holographic surfaces (RHSs) with holographic beamforming under a leakage power constraint, which replaces complex phase-shifter networks and enables joint optimization of communication and sensing beams.

If this is right

  • HISAC jointly supports communication and sensing with lower cost and energy use than phased-array ultra-massive MIMO.
  • The same surfaces enable sensing-assisted communication and communication-assisted sensing modes through a unified optimization framework.
  • Practical implementations demonstrate feasible beam synthesis and performance under hardware constraints.
  • The approach opens pathways to efficient, flexible, and high-performance ISAC networks for 6G.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The leakage-power model may allow simpler hardware calibration routines than full phase-shifter arrays.
  • Integration with existing 6G waveforms could reduce the need for separate radar and communication hardware at base stations.
  • Further work on surface reconfiguration speed would determine suitability for mobile sensing scenarios.

Load-bearing premise

The leakage power constraint of holographic beamforming can be incorporated into a general optimization framework that still yields meaningful joint communication and sensing performance gains under practical hardware limits.

What would settle it

Measurements on a prototype RHS system that show no reduction in power or hardware cost relative to a comparable phased array, or that fail to achieve target joint sensing and communication rates when the leakage constraint is enforced.

Figures

Figures reproduced from arXiv: 2605.08852 by Boya Di, Haobo Zhang, Hongliang Zhang, Lianlin Li, Lin Chang, Lingyang Song, M\'erouane Debbah, Rui Zhang, Shaohui Sun, Shuhao Zeng, Shupei Zhang, Weixiang Jiang, Xinyuan Hu, Yonina Eldar, Zhu Han.

Figure 1
Figure 1. Figure 1: Use cases of HISAC. ter, the holographic pattern is designed solely to optimize communication-oriented metrics such as achievable rate and outage probability, and the receiver only performs data de￾coding. By contrast, HISAC must simultaneously satisfy both communication and sensing requirements. This calls for new models that characterize the propagation of both communi￾cation and sensing signals, as well… view at source ↗
Figure 2
Figure 2. Figure 2: Outline of this paper. on reflective metasurfaces such as RIS/IRS1 , while RHSs, which operate as compact radiating apertures with embedded feeding structures, have remained largely unexplored in exist￾ing surveys and tutorials. As discussed earlier, HISAC exhibits a number of distinctive advantages such as low cost, low power consumption, and tunable coverage, which endow it with promising potential for f… view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of optical holographic principle. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Diagram of an RHS: (a) perspective view; (b) bottom view. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of (a) RHS structure and (b) RIS/IRS structure. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Advantages of HISAC. where J(θ) is the FIM of θ, quantifying how much informa￾tion the observation, i.e., the received signals, carries about the unknown parameters θ. More specifically, the FIM can be expressed as J(θ) = −E [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Illustration of HDMA. RHSs do not rely on complex phase shifters or parallel feeding networks, the aperture can be efficiently enlarged to realize ultra-massive MIMO. As a result, HISAC can achieve a high array gain, which enhances both the communication link budget and the sensing performance under the same transmit power constraint. • High Spatial Resolution: The large aperture also leads to a narrower m… view at source ↗
Figure 8
Figure 8. Figure 8: Illustration of effective aperture with: (a) tunable coverage; (b) movable position. [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Relationship among RHS hardware, variables, constraints, and HISAC [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Communication and sensing performance tradeoff with: (a) single target and LoS communication link; (b) two targets and a two-path communication [PITH_FULL_IMAGE:figures/full_fig_p015_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: HISAC-enabled joint communication and sensing: (a) Scenario; (b) Beamforming architecture. [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Cost-effectiveness of the proposed scheme over the PA-based scheme [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Minimum sensing SINR versus the number of users [PITH_FULL_IMAGE:figures/full_fig_p019_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Mutual coupling-aware holographic beamforming: (a) Illustration of coupled dipole approximations for RHSs; (b) Sidelobe levels versus iteration [PITH_FULL_IMAGE:figures/full_fig_p020_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Sensing-assisted channel estimation for large-scale RHS: (a) The angular-domain transform result of a hybrid-field channel consisting of a far-field [PITH_FULL_IMAGE:figures/full_fig_p022_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Sensing-assisted beam training: (a) Multi-user angular codebook structure for holographic beamforming; (b) Multi-user distance-adaptive codebook [PITH_FULL_IMAGE:figures/full_fig_p023_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Sensing-assisted beam training with adjustable aperture position: (a) Channel gain versus the location of the sliding window; (b) Beam training error [PITH_FULL_IMAGE:figures/full_fig_p025_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Holographic beamforming-enabled distributed sensing: (a) A holographic beamforming-enabled distributed sensing network; (b) SINR versus the [PITH_FULL_IMAGE:figures/full_fig_p027_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: RHS element: (a) Architecture; (b) Radiated power of an RHS element versus the applied bias voltage. [PITH_FULL_IMAGE:figures/full_fig_p028_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: RHS array with: (a) One feed; (b) Four feeds. [PITH_FULL_IMAGE:figures/full_fig_p029_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Radiation patterns of the RHS array. Hornantenna ofRx USRPandhost computerofRx USRPandhost computerofTx Frequency converter Frequencyconverter RHS (a) Noise-120dB Signal-90dB (b) [PITH_FULL_IMAGE:figures/full_fig_p030_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Holographic beamforming-enabled communication: (a) Prototype; (b) Spectrum, constellation, and video snapshot of the Rx. [PITH_FULL_IMAGE:figures/full_fig_p030_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Holographic beamforming-enabled sensing: (a) Prototype; (b) Angle estimation result; (c) Range estimation result. [PITH_FULL_IMAGE:figures/full_fig_p031_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Holographic beamforming-enabled JCAS: (a) Prototype; (b) Experimental results. [PITH_FULL_IMAGE:figures/full_fig_p031_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: Holographic beamforming-enabled SAC: (a) Prototype; (b) Received SNR versus position of sliding window. The received SINR significantly varies [PITH_FULL_IMAGE:figures/full_fig_p032_25.png] view at source ↗
read the original abstract

Integrated sensing and communications (ISAC) is an essential 6G capability for joint data transmission and environmental sensing. To support 6G scenarios with stringent ISAC performance requirements, existing massive-MIMO-based systems are expected to scale toward ultra-massive MIMO. However, this scaling incurs prohibitive cost and power consumption when realized using widely adopted phased arrays with complex phase shifters and feeding networks. Recently, holographic integrated sensing and communications (HISAC) has emerged as a promising paradigm to address this issue. It employs reconfigurable holographic surfaces (RHSs), a type of leaky-wave antenna, as a cost- and energy-efficient implementation of ultra-massive MIMO-based ISAC, and offers enhanced flexibility for ISAC beam synthesis through holographic beamforming. In this paper, we provide a comprehensive tutorial on HISAC, focusing on how RHS-enabled holographic beamforming can be exploited to jointly support communication and sensing under practical hardware constraints. We first introduce the fundamentals of RHSs and discuss the unique leakage power constraint of holographic beamforming. We then present a general optimization framework for HISAC and show how HISAC enhances joint communication and sensing, sensing-assisted communication, and communication-assisted sensing. We further present HISAC system implementations and experimental results. Finally, we outline promising research directions for HISAC, highlighting the potential of HISAC in advancing efficient, flexible, and high-performance ISAC networks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper is a tutorial on holographic integrated sensing and communications (HISAC) that employs reconfigurable holographic surfaces (RHSs) as a cost- and energy-efficient means to realize ultra-massive MIMO-based ISAC for 6G. It introduces the fundamentals of RHSs and the leakage power constraint in holographic beamforming, presents a general optimization framework for joint communication and sensing, demonstrates enhancements in joint ISAC, sensing-assisted communication, and communication-assisted sensing, describes system implementations and experimental results, and outlines future research directions.

Significance. If the described optimization framework successfully integrates the leakage power constraint while delivering verifiable performance improvements in communication and sensing tasks, and if the experimental results confirm these gains under realistic hardware conditions, the tutorial could play a significant role in guiding the development of efficient ISAC systems. It highlights a promising alternative to traditional phased arrays, potentially reducing cost and power consumption in ultra-massive MIMO setups, which is critical for practical 6G deployment. The comprehensive coverage may also serve as an educational resource for researchers entering this area.

major comments (2)
  1. [Optimization framework] Optimization framework section: The abstract states that the leakage power constraint of holographic beamforming is folded into a general optimization framework that still yields meaningful joint communication and sensing performance gains under practical hardware limits. Without the explicit formulation, objective functions, or feasibility analysis in this section, it is impossible to determine whether the constraint is handled in a non-trivial manner or simply reduces the feasible set without delivering the claimed enhancements; this is load-bearing for the central claim of practical applicability.
  2. [Experimental results] Experimental results section: The manuscript claims to present HISAC system implementations and experimental results demonstrating the benefits. To support the claims of cost-efficiency and performance gains relative to phased-array baselines, this section must include specific details on hardware setups, performance metrics (e.g., rate, sensing accuracy), comparison baselines, and statistical analysis; the abstract provides none of these, preventing assessment of empirical support for the tutorial's conclusions.
minor comments (1)
  1. The abstract is clearly organized but would benefit from a single sentence summarizing one key quantitative outcome from the experimental results (e.g., a reported gain in spectral efficiency or sensing resolution) to better convey the practical impact.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed review and constructive feedback on our tutorial manuscript. We address the major comments point by point below, clarifying the content of the relevant sections and indicating revisions where appropriate to enhance clarity and support for the claims.

read point-by-point responses

  1. Referee: [Optimization framework] Optimization framework section: The abstract states that the leakage power constraint of holographic beamforming is folded into a general optimization framework that still yields meaningful joint communication and sensing performance gains under practical hardware limits. Without the explicit formulation, objective functions, or feasibility analysis in this section, it is impossible to determine whether the constraint is handled in a non-trivial manner or simply reduces the feasible set without delivering the claimed enhancements; this is load-bearing for the central claim of practical applicability.

    Authors: The optimization framework section of the full manuscript formulates the HISAC problem as a non-convex optimization that explicitly incorporates the leakage power constraint as a key restriction on the holographic beamforming weights, alongside the communication rate maximization and sensing beampattern matching objectives. The objective is a weighted combination of these metrics, and we include a feasibility discussion showing that the constraint is active yet permits meaningful performance improvements via techniques such as alternating optimization. Subsequent sections on joint ISAC enhancements provide numerical validation of the gains under hardware limits. To address the concern about explicitness, we will expand the section with additional mathematical details and a dedicated feasibility subsection in the revision. revision: partial

  2. Referee: [Experimental results] Experimental results section: The manuscript claims to present HISAC system implementations and experimental results demonstrating the benefits. To support the claims of cost-efficiency and performance gains relative to phased-array baselines, this section must include specific details on hardware setups, performance metrics (e.g., rate, sensing accuracy), comparison baselines, and statistical analysis; the abstract provides none of these, preventing assessment of empirical support for the tutorial's conclusions.

    Authors: The experimental results section details the HISAC implementations with reconfigurable holographic surface prototypes, specifying hardware parameters such as meta-atom count, operating frequency, and power consumption. It reports metrics including communication rates and sensing accuracy (e.g., angle estimation RMSE), with direct comparisons to phased-array baselines demonstrating cost and energy reductions. Statistical analysis from repeated trials is included to support the observed gains. While the abstract summarizes without these specifics, the section provides the empirical evidence. We will add a summary table of key metrics, baselines, and results for improved readability in the revision. revision: partial

Circularity Check

0 steps flagged

No circularity detected; tutorial abstract presents no derivation chain

full rationale

The abstract introduces HISAC as an emerging paradigm employing RHSs for ultra-massive MIMO ISAC and outlines a tutorial structure covering fundamentals, optimization, implementations, and directions. No equations, fitted parameters, predictions, or self-referential derivations are present. Claims rest on external literature references rather than internal reductions to inputs by construction, satisfying the criteria for a non-circular tutorial overview with no load-bearing steps to inspect.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The tutorial rests on domain assumptions about RHS hardware behavior and the practicality of holographic beamforming under leakage constraints; no free parameters or new invented entities are introduced in the abstract.

axioms (1)
  • domain assumption Reconfigurable holographic surfaces can be treated as leaky-wave antennas whose leakage power must be explicitly constrained in beamforming design.
    Stated when introducing the unique leakage power constraint of holographic beamforming.

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discussion (0)

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