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arxiv: 2605.01838 · v1 · submitted 2026-05-03 · 📡 eess.SP

Waveform Index Modulation for Backscatter Communications in RIS-Based MIMO Radars

Mehri Nikzad , Luca Venturino , Emanuele Grossi , Xiaodong Wang This is my paper

Pith reviewed 2026-05-09 16:38 UTC · model grok-4.3

classification 📡 eess.SP
keywords reconfigurable intelligent surfacebackscatter communicationindex modulationMIMO radarwaveform modulationsymbiotic radar-communication
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The pith

A reconfigurable intelligent surface can assist MIMO radar while sending data to a reader by selecting unordered subsets of orthogonal phase codes.

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

The paper shows how to partition an RIS into subarrays that both steer a radar signal toward a target sector and superimpose slow-time orthogonal phase codes. Information is carried by choosing which unordered subset of those codes to apply, so the overall transmit beampattern stays the same. At the reader this choice can be detected with low complexity and without any channel state information. The scheme therefore lets the same surface perform radar illumination and backscatter communication at the same time.

Core claim

Partitioning the RIS into subarrays and superimposing slow-time orthogonal phase codes lets the surface redirect the radar waveform while encoding communication bits in the selection of an unordered subset of those codes; the resulting backscatter signal reaches the reader without changing the radar transmit beampattern, and the index choice is recoverable by low-complexity detection that requires no channel state information.

What carries the argument

Waveform index modulation realized by unordered subset selection of slow-time orthogonal phase codes applied to RIS subarrays.

If this is right

  • The radar's angular coverage and resolution remain identical to a conventional MIMO radar.
  • The reader recovers the transmitted bits without estimating any propagation channels.
  • The same RIS hardware performs both radar assistance and communication without extra time or frequency resources.
  • Detection complexity scales with the number of code subsets rather than with channel estimation overhead.

Where Pith is reading between the lines

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

  • The scheme could be extended to dynamic subarray sizes that trade radar resolution against communication rate on the fly.
  • If the codes lose orthogonality in real hardware, the preserved-beampattern property would need re-validation.
  • The low-complexity detector could serve as a building block for multi-user readers that each decode their own subset index.

Load-bearing premise

Dividing the RIS into subarrays and applying orthogonal phase codes keeps the radar beampattern unchanged no matter which subset is chosen.

What would settle it

An experiment or simulation in which the measured radar beampattern visibly changes, or the reader's detection error rate rises sharply, when different code subsets are selected.

Figures

Figures reproduced from arXiv: 2605.01838 by Emanuele Grossi, Luca Venturino, Mehri Nikzad, Xiaodong Wang.

Figure 1
Figure 1. Figure 1: Considered system architecture. at the reader. Sec. IV presents the numerical analysis. Finally, concluding remarks are given in Sec. V. II. SYSTEM DESCRIPTION We consider the symbiotic system in view at source ↗
Figure 2
Figure 2. Figure 2: Proposed detector at the reader. beampattern. To make this explicit, let ψ(θ) ∈ UMRIS denote the RIS steering vector toward direction θ = [θ az; θ el], where θ az and θ el are the azimuth and elevation angles, respectively. Then, the energy radiated by the RIS toward direction θ over one frame interval is proportional to B(θ) = ∥X(γST ⊙ ψ(θ))∥ 2 = L X N n=1 view at source ↗
Figure 3
Figure 3. Figure 3: Transmission rate R versus codeword length L. 15 20 25 30 35 40 L 10!10 10!8 10!6 10!4 10!2 Pe SNR = 4; 5; 6; 7; 8 dB view at source ↗
Figure 4
Figure 4. Figure 4: Error probability Pe versus codeword length L for different SNRs. independently generated. The complex amplitude is modeled as the sum of a specular and a diffuse components [27], i.e., γTR,q = σTR r κTR 1 + κTR e jϕTR,q + r 1 1 + κTR gTR,q , (21) where σ 2 TR denotes the average power, κTR is the power ratio between the specular and diffuse components, ϕTR,q is uniformly distributed in [0, 2π), and gTR,… view at source ↗
Figure 5
Figure 5. Figure 5: Error probability Pe versus normalized delay spread (∆max TR − ∆min TR )W for different SNRs. values of the signal-to-noise ratio (SNR), defined as SNR = PGB(θ¯)σ 2 TR LNσ2 R,ω . (22) Results are averaged over 106 realizations of the TR channel. For all considered SNR values, Pe decreases as L increases. This behavior is due to the coherent processing gain provided by longer codewords, which enhances the s… view at source ↗
read the original abstract

This paper studies a symbiotic system in which a reconfigurable intelligent surface (RIS) assists a radar transmitter while conveying information to a reader via backscattering. The RIS is partitioned into subarrays that redirect the radar signal toward the angular sector under inspection and superimpose a slow-time modulation using orthogonal phase codes, thereby implementing MIMO radar functionalities. Communication is achieved by encoding information in the selection of an unordered subset of orthogonal codewords, without altering the RIS transmit beampattern. At the reader, the proposed index modulation scheme enables low-complexity detection without requiring channel state information. Numerical results demonstrate the effectiveness of the proposed backscatter communication approach.

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 / 2 minor

Summary. The paper proposes a symbiotic RIS-assisted MIMO radar system in which the RIS is partitioned into subarrays that redirect the incident radar signal while superimposing slow-time orthogonal phase codes to realize MIMO radar functionality. Information is encoded via unordered subset selection of these codes for backscatter communication to a reader; the scheme is claimed to leave the radar transmit beampattern unchanged and to permit low-complexity subset detection at the reader without channel state information. Numerical results are presented to demonstrate effectiveness.

Significance. If the central invariance claims hold, the work would offer a low-overhead mechanism for embedding backscatter communication into RIS-based MIMO radars without compromising the radar beampattern or requiring CSI at the reader, potentially advancing integrated sensing and communication designs. The index-modulation approach on orthogonal slow-time codes is a distinctive technical choice that could reduce detection complexity relative to conventional backscatter methods.

major comments (2)
  1. [system model and detection sections] The central claim that subset selection of orthogonal codes preserves the radar transmit beampattern while enabling CSI-free detection relies on an unproven invariance: when subarrays occupy distinct spatial positions their effective channels to the reader are distinct complex scalars, so the received signal is a linear combination of the selected codes each scaled by an unknown coefficient. No analytical guarantee is supplied that a detection rule can recover the unordered subset without estimating these coefficients or assuming identical channels (see the system model and detection sections).
  2. [numerical results section] Numerical results are invoked to demonstrate effectiveness, yet the manuscript provides no details on the simulation setup, channel realizations, error-rate analysis, or comparison against CSI-aware baselines that would allow verification of the low-complexity CSI-free claim (see the numerical results section).
minor comments (2)
  1. Notation for the orthogonal phase codes and the unordered subset selection should be introduced with explicit definitions and an example for small numbers of subarrays to improve readability.
  2. The abstract states that the RIS 'redirect[s] the radar signal toward the angular sector under inspection'; a brief reference to the underlying RIS phase-shift design or beampattern formula would clarify how the partitioning interacts with this redirection.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We appreciate the referee's thorough review and constructive feedback on our manuscript. We address the major comments point by point below, providing clarifications and indicating revisions where necessary to strengthen the paper.

read point-by-point responses

  1. Referee: [system model and detection sections] The central claim that subset selection of orthogonal codes preserves the radar transmit beampattern while enabling CSI-free detection relies on an unproven invariance: when subarrays occupy distinct spatial positions their effective channels to the reader are distinct complex scalars, so the received signal is a linear combination of the selected codes each scaled by an unknown coefficient. No analytical guarantee is supplied that a detection rule can recover the unordered subset without estimating these coefficients or assuming identical channels (see the system model and detection sections).

    Authors: We thank the referee for highlighting this important point regarding the detection mechanism. The proposed detection exploits the orthogonality of the phase codes to decouple the contributions from each subarray. By computing the inner product of the received signal with each orthogonal code, we obtain the effective channel coefficient multiplied by an indicator of selection. Since the channels are assumed non-zero and the detection identifies the support (the unordered subset) based on which projections exceed a noise-dependent threshold, explicit estimation of the coefficients is not required. We concede that a rigorous analytical proof of the detection performance under distinct unknown channels was not provided in the original manuscript. In the revision, we will add a subsection deriving the pairwise error probability and showing that the scheme achieves reliable detection without CSI, under the assumption of independent fading channels. This will substantiate the invariance claim. revision: partial

  2. Referee: [numerical results section] Numerical results are invoked to demonstrate effectiveness, yet the manuscript provides no details on the simulation setup, channel realizations, error-rate analysis, or comparison against CSI-aware baselines that would allow verification of the low-complexity CSI-free claim (see the numerical results section).

    Authors: We agree that the numerical results section requires more comprehensive details to support the claims. In the revised manuscript, we will provide: a complete description of the simulation parameters (e.g., number of RIS elements, subarray sizes, code length, carrier frequency, and path loss models); the channel generation process (including specific fading distributions for the radar-RIS, RIS-target, and RIS-reader links); the Monte Carlo setup for bit error rate curves; and performance comparisons against a CSI-aware maximum likelihood detector as a benchmark. These additions will enable independent verification of the low-complexity CSI-free detection performance. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper describes a proposed index modulation scheme that partitions an RIS into subarrays, applies slow-time orthogonal phase codes for MIMO radar functionality, and encodes information via unordered subset selection of codewords. The claims of beampattern preservation and low-complexity CSI-free detection at the reader are presented as direct consequences of this design choice, supported by numerical results rather than any self-referential fitting, parameter renaming, or load-bearing self-citation chains. No equations or steps in the provided description reduce to their own inputs by construction, and the derivation remains self-contained against external benchmarks of RIS partitioning and orthogonal coding techniques.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available, so no explicit free parameters, axioms, or invented entities are identifiable. The proposal appears to rely on standard domain assumptions from RIS and MIMO radar literature without introducing new postulated entities.

pith-pipeline@v0.9.0 · 5407 in / 1126 out tokens · 35275 ms · 2026-05-09T16:38:33.692890+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

27 extracted references · 27 canonical work pages

  1. [1]

    Ambient backscatter communications: A contempo- rary survey,

    N. V . Huynhet al., “Ambient backscatter communications: A contempo- rary survey,”IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 2889–2922, Fourthquarter 2018

    work page 2018

  2. [2]

    Communication by means of reflected power,

    H. Stockman, “Communication by means of reflected power,”Proceed- ings of the IRE, vol. 36, no. 10, pp. 1196–1204, Oct. 1948

    work page 1948

  3. [3]

    Antennas and propagation in UHF RFID systems,

    P. V . Nikitin and K. V . S. Rao, “Antennas and propagation in UHF RFID systems,” inProc. IEEE Int. Conf. RFID, Las Vegas, NV , USA, Apr. 2008, pp. 277–288

    work page 2008

  4. [4]

    Ambient backscatter: Wireless communication out of thin air,

    V . Liuet al., “Ambient backscatter: Wireless communication out of thin air,” inProc. of ACM SIGCOMM, Hong Kong, China, Aug. 2013, p. 39–50

    work page 2013

  5. [5]

    Backscatter communication assisted by reconfigurable intelligent surfaces,

    Y .-C. Lianget al., “Backscatter communication assisted by reconfigurable intelligent surfaces,”Proceedings of the IEEE, vol. 110, no. 9, pp. 1339– 1357, Sep. 2022

    work page 2022

  6. [6]

    Reconfigurable intelligent surfaces: Principles and opportunities,

    Y . Liuet al., “Reconfigurable intelligent surfaces: Principles and opportunities,”IEEE Communications Surveys & Tutorials, vol. 23, no. 3, pp. 1546–1577, thirdquarter 2021

    work page 2021

  7. [7]

    Wireless communications with space–time modulated metasurfaces,

    M. Mizmizi, D. Tagliaferri, and U. Spagnolini, “Wireless communications with space–time modulated metasurfaces,”IEEE Journal on Selected Areas in Communications, vol. 42, no. 6, pp. 1534–1548, Jun. 2024

    work page 2024

  8. [8]

    Integrated sensing and communication based on space-time-coding metasurfaces,

    X. Q. Chenet al., “Integrated sensing and communication based on space-time-coding metasurfaces,”Nature Communications, vol. 16, no. 1, p. 1836, 2025

    work page 2025

  9. [9]

    Wireless communications through reconfigurable intelligent surfaces,

    E. Basaret al., “Wireless communications through reconfigurable intelligent surfaces,”IEEE Access, vol. 7, pp. 116 753–116 773, 2019

    work page 2019

  10. [10]

    Active RIS-aided ISAC systems: Beamforming design and performance analysis,

    Z. Yu, X. Wang, X. Mu, Y . Liu, and R. Schober, “Active RIS-aided ISAC systems: Beamforming design and performance analysis,”IEEE Transactions on Communications, vol. 72, no. 3, pp. 1578–1595, Mar. 2023

    work page 2023

  11. [11]

    Reconfigurable intelligent surfaces for N-LOS radar surveillance,

    A. Aubry, A. D. Maio, and M. Rosamilia, “Reconfigurable intelligent surfaces for N-LOS radar surveillance,”IEEE Transactions on V ehicular Technology, vol. 70, no. 10, pp. 10 735–10 749, Oct. 2021

    work page 2021

  12. [12]

    Foundations of MIMO radar detection aided by reconfigurable intelligent surfaces,

    S. Buzzi, E. Grossi, M. Lops, and L. Venturino, “Foundations of MIMO radar detection aided by reconfigurable intelligent surfaces,”IEEE Transactions on Signal Processing, vol. 70, pp. 1749–1763, 2022

    work page 2022

  13. [13]

    Integrated sensing and communications with reconfigurable intelligent surfaces: From signal modeling to processing,

    S. P. Chepuriet al., “Integrated sensing and communications with reconfigurable intelligent surfaces: From signal modeling to processing,” IEEE Signal Processing Magazine, vol. 40, no. 6, pp. 41–62, Sep. 2023

    work page 2023

  14. [14]

    ISAC STAR-RIS transceivers with space-time coded pulsed signals,

    H. Taremizadeh, E. Grossi, L. Venturino, and M. Lops, “ISAC STAR-RIS transceivers with space-time coded pulsed signals,”IEEE Open Journal of the Communications Society, vol. 6, pp. 9569–9586, 2025

    work page 2025

  15. [15]

    Integrated sensing and communication for RIS-assisted backscatter systems,

    X. Wang, Z. Fei, and Q. Wu, “Integrated sensing and communication for RIS-assisted backscatter systems,”IEEE Internet of Things Journal, vol. 10, no. 15, pp. 13 716–13 726, Aug. 2023

    work page 2023

  16. [16]

    Reconfigurable intelligent surfaces enabled vehicular communications: A comprehensive survey of recent advances and future challenges,

    M. Denget al., “Reconfigurable intelligent surfaces enabled vehicular communications: A comprehensive survey of recent advances and future challenges,”IEEE Transactions on Intelligent V ehicles, vol. 10, no. 8, pp. 4191–4216, Aug 2025

    work page 2025

  17. [17]

    Symbiotic radio: Cognitive backscattering communications for future wireless networks,

    Y .-C. Liang, Q. Zhang, E. G. Larsson, and G. Y . Li, “Symbiotic radio: Cognitive backscattering communications for future wireless networks,” IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 4, pp. 1242–1255, Dec 2020

    work page 2020

  18. [18]

    Toward full passive internet of things: Symbiotic localization and ambient backscatter communication,

    C. Ren and L. Liu, “Toward full passive internet of things: Symbiotic localization and ambient backscatter communication,”IEEE Internet of Things Journal, vol. 10, no. 22, pp. 19 495–19 506, Nov 2023

    work page 2023

  19. [19]

    Radar- enabled ambient backscatter communications,

    L. Venturino, E. Grossi, M. Lops, J. Johnston, and X. Wang, “Radar- enabled ambient backscatter communications,”IEEE Transactions on Wireless Communications, vol. 22, no. 12, pp. 8666–8680, Dec. 2023

    work page 2023

  20. [20]

    Semi- blind multi-tag ambient backscatter communications using radar signals,

    L. Venturino, E. Grossi, J. Johnston, M. Lops, and X. Wang, “Semi- blind multi-tag ambient backscatter communications using radar signals,” IEEE Transactions on Wireless Communications, vol. 23, no. 12, pp. 19 870–19 884, Dec. 2024

    work page 2024

  21. [21]

    MIMO radar with colocated antennas,

    J. Li and P. Stoica, “MIMO radar with colocated antennas,”IEEE Signal Processing Magazine, vol. 24, no. 5, pp. 106–114, 2007

    work page 2007

  22. [22]

    Li and P

    J. Li and P. Stoica,MIMO Radar Signal Processing. New York, NY , USA: John Wiley Sons, 2009

    work page 2009

  23. [23]

    W. L. Stutzman and G. A. Thiele,Antenna Theory and Design, 3rd ed. New York, NY , USA: John Wiley Sons, 2012

    work page 2012

  24. [24]

    Beampattern design for transmit architectures based on reconfigurable intelligent surfaces,

    C. D’Elia, E. Grossi, and L. Venturino, “Beampattern design for transmit architectures based on reconfigurable intelligent surfaces,”IEEE Transactions on V ehicular Technology, vol. 74, no. 12, pp. 19 323–19 338, Dec. 2025

    work page 2025

  25. [25]

    Noncoherent detection for ambient backscatter systems,

    J. Qian, F. Gao, G. Wang, S. Jin, and H. Zhu, “Noncoherent detection for ambient backscatter systems,”IEEE Transactions on Wireless Communications, vol. 16, no. 3, pp. 1412–1422, Mar. 2017

    work page 2017

  26. [26]

    Papoulis and S

    A. Papoulis and S. U. Pillai,Probability, Random V ariables, and Stochastic Processes, 4th ed. New York: McGraw-Hill, 2002

    work page 2002

  27. [27]

    Generalized radar clutter model,

    D. A. Shnidman, “Generalized radar clutter model,”IEEE Transactions on Aerospace and Electronic Systems, vol. 35, no. 3, pp. 857–865, Jul. 1999. © 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses

    work page 1999