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REVIEW 4 major objections 5 minor 73 references

Hybrid RISs for Simultaneous Tunable Reflections and Sensing

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A metasurface that both reflects and senses can recover individual channels with far fewer pilots than one that only reflects.

desk verdict Solid review of the HRIS concept, but the central pilot-count claim is not proven as stated and the ISAC equations have real typos; needs major revision. read the letter →

arxiv 2507.16550 v1 pith:VGZF36RG submitted 2025-07-22 eess.SP

George C. Alexandropoulos , Nir Shlezinger , Ioannis Gavras , Haiyang Zhang This is my paper
classification eess.SP
keywords hybridreconfigurableintelligentsurfaceintegratedsensingandcommunicationschannelestimationpilotoverheadmeta-atompowersplittingpositionerrorboundMIMOself-configuringRIS
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This chapter establishes that a reconfigurable intelligent surface whose meta-atoms both reflect a controllable portion of the incident wave and sense the rest (a hybrid RIS, or HRIS) can simultaneously shape the wireless channel and observe it. The quantitative payoff is that with $N$ meta-atoms and $N_r$ reception chains, the individual HRIS-to-user and BS-to-HRIS channels are recoverable from $\tau \ge N\max\{1, K/N_r\}$ pilots, whereas in the paper's example more than 90 pilots are needed to estimate only the cascaded channel. This matters because individual channel estimates, not just cascaded ones, are what enable coherent communication, self-configuring surfaces, localization, and integrated sensing and communications.

What carries the argument

The load-bearing object is the hybrid meta-atom and its linear split model: element $l$ reflects $\rho_l e^{j\psi_l} r_l$ and forwards $(1-\rho_l)e^{j\phi_{r,l}} r_l$ to reception chain $r$, collected in the diagonal matrix $\boldsymbol{\Psi}(\boldsymbol{\rho},\boldsymbol{\psi})$ and the combining matrix $\boldsymbol{\Phi}(\boldsymbol{\rho},\boldsymbol{\phi})$. These matrices turn channel estimation into a rank problem: the Kronecker-structured observation matrices built from them become full-column-rank precisely at the pilot count of Proposition 1, and the MMSE expressions of Theorems 1 and 2 are explicit functions of the same configuration parameters, which is what gives the HRIS the information it needs to configure itself.

What would settle it

Measure the reflected port and the sensing waveguide port of a fabricated hybrid meta-atom across bias voltages and frequency; if the reflected phase $\psi_l$ and the sensed phase $\phi_{r,l}$ cannot be tuned independently, or if the splitting ratio $\rho_l$ changes appreciably with frequency, the full-column-rank arguments behind Proposition 1 collapse.

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Extended reading notes

Core claim

The central discovery is that a single metasurface can split each incident signal into a phase-shifted, tunable reflected component and a sensed component routed to local reception chains, and that this split is enough to disentangle the two channel legs that a purely reflective RIS mixes. With reflection matrix $\boldsymbol{\Psi}(\boldsymbol{\rho},\boldsymbol{\psi})$ and sensing combining matrix $\boldsymbol{\Phi}(\boldsymbol{\rho},\boldsymbol{\phi})$ defined by the per-element split $\rho_l$, the paper proves in Proposition 1 that the stacked observation matrices become full-column-rank once $\tau \ge N\max\{1, K/N_r\}$, so the HRIS-to-users channel $\mathbf{G}$ and the BS-to-HRIS channel $\mathbf{H}_{\mathrm{BR}}$ can each be recovered exactly, with the same pilots serving both estimates. It then derives MMSE estimators for both channels under noise (Theorems 1 and 2) and, in the integrated sensing and communications application, maximizes downlink rate subject to a position error bound over an area of interest, showing numerically that the power-splitting ratio $\rho$ trades communication quality against sensing accuracy.

Load-bearing premise

Everything rests on the linear split model of equations (1)-(5): each meta-atom sends a controllable fraction of its incident signal to the reflected path and the rest to the sensing path with independently controllable phases, with no mutual coupling, calibration errors, or frequency dependence, and for the sensing application the HRIS also knows the base station's position well enough to subtract the static channel.

Editorial extensions

If this is right

  • With $N=64$ elements, $N_r=8$ reception chains, and $K=8$ users, both individual channels are recoverable from 64 pilots, while the cascaded-channel scheme in [44] needs over 90; the saved pilots translate directly into spectral efficiency.
  • Because the same pilots are reused for the HRIS-side and BS-side estimates, the sensing capability does not double the training overhead.
  • The MSE formulas give the HRIS controller a handle to choose its power-splitting and phase configuration, and the numerical trade-off shows that reflecting up to about half the power improves BS-side estimation while barely hurting HRIS-side estimation.
  • In the ISAC application, a bistatic position error bound can be enforced across an area of interest while maximizing downlink rate, so one HRIS can communicate and localize simultaneously.
  • The more reception RF chains $N_r$ the HRIS carries, the better it estimates even the cascaded channel, at the price of hardware complexity and power consumption.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct but unstated consequence is that the full-rank argument should transfer to any reconfigurable aperture that taps a fraction of its elements into a separate readout channel: one pilot sequence can feed two independent estimates, so the benefit is structural rather than specific to this circuit.
  • If the linear-split hardware is realized at scale, the training phase could be extended to joint channel-and-position estimation, because the sensed observations already carry angle-of-arrival information and could feed both the BS channel estimate and a local map used by the surface for its own beam steering.
  • A testable extension would replace the noise-free pilot bound with an optimal allocation of $\rho_l$ per element and per sub-frame; the MSE expressions support such an optimization, but the chapter does not carry it out.
  • The 64-versus-90 pilot comparison is for one configuration; a sweep over pilot count and received power would place the HRIS benefit and the reflective-RIS baseline on a common performance curve.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper reviews the emerging concept of Hybrid Reflecting and Sensing RISs (HRISs), in which each meta-atom splits an incident signal into a tunably reflected portion and a portion that is sensed through waveguides and RF chains. It proposes a mathematical model for this dual operation and studies two applications: downlink ISAC with a PEB constraint, and uplink estimation of the individual UT-HRIS and HRIS-BS channels. The central analytical claims are Proposition 1, which gives a noise-free pilot-count bound for recovering both channel matrices, and Theorems 1--2, which give MMSE expressions for noisy channel estimation. Numerical results in Sections 4.1.3 and 4.2.4 illustrate the trade-off between reflection and sensing. The manuscript is written as a chapter-length review with substantial hardware discussion and self-contained derivations.

Significance. If the stated results were correct, the paper would provide a quantitative case for HRISs: Proposition 1 claims that roughly N max{1, K/N_r} pilots suffice to recover the individual channels, which can be significantly less than the overhead of cascaded-channel estimation for reflective RISs. The hardware feasibility discussion, including the full-wave simulation cited from [30], is a useful contribution. The explicit MSE formulas in Theorems 1--2 are also valuable for system design. However, the manuscript contains several load-bearing technical errors: Proposition 1's proof omits the frame constraint T >= K, and the ISAC section has constraint and formula inconsistencies. These issues currently undermine the central quantitative claims, although they appear local and fixable rather than fatal to the overall concept.

major comments (4)
  1. [Section 4.2.2, Proposition 1] The proof only counts dimensions of A1 and A2 and ignores the rank limitations imposed by the Kronecker and pilot-structure factors. Since orthogonal pilots require T >= K, the matrix A1 = s^T ⊗ A_RC has rank at most K * min(N_r B, N), so recovering G (with NK unknowns) requires N_r B >= N, i.e., τ = BT >= N T/N_r. Similarly, the stacked matrix in A2 has rank at most min(T, B K), so recovering HBR requires B K >= N, i.e., τ >= N T/K. For any T > K, both required pilot lengths exceed the claimed bound τ >= N max{1, K/N_r}. Concretely, with N=64, N_r=8, K=8, T=16, B=4, one has τ=64 satisfying (15), yet N_r B = 32 < 64 and B K = 32 < 64, so neither channel is identifiable. Thus Proposition 1 as stated is false for frame designs with T > K; it is only valid under the additional assumption T = K, which is absent from the statement and is inconsistent with the T=70-pilot setup of Section 4.2.4. This is a central claim of the paper and needs to be corrected or qualified.
  2. [Section 4.1.2, OP constraints] The optimization problem OP imposes the constraints |[Φ]_{r,l}| = 1 and |[Ψ]_{l,l}| = 1 for all r,l. This contradicts the definitions given in Section 4.1.1, where [Φ]_{j,(r-1)N_E+l} = (1-ρ)e^{jφ_{r,l}} and Ψ = ρ diag(e^{jψ_1},...,e^{jψ_N}). For a power splitting ratio ρ in (0,1), these moduli are 1-ρ and ρ, respectively, not 1. As written, OP is infeasible for all nontrivial ρ, so the optimization and the numerical results in Fig. 4 are not supported by the stated model. The constraints should be replaced by unit-modulus constraints on the phase-only factors, or the definitions of Φ and Ψ must be revised.
  3. [Section 4.1.2, FIM and downlink channel formula] Two equation-level errors affect the ISAC formulation. First, the FIM expression [J]_{i,j} = 2ρ^2 \bar{T}/σ^2 Re(v^H ∂H_RB^H/∂[ξ]_i ΦΦ^H ∂H_RB/∂[ξ]_j v) contains a factor ρ^2, but the entries of Φ are (1-ρ)e^{jφ}, so the correct factor is (1-ρ)^2 (unless Φ is redefined). Second, the downlink channel estimate bh_DL = bh_UB + (1-ρ)bh_UR Ψ bh_RB double-counts ρ because Ψ already includes the factor ρ (Ψ = ρ diag(e^{jψ})). These errors propagate into the optimized design and into the reported trade-off curves of Fig. 4, so they cannot be dismissed as mere notation.
  4. [Section 4.2.3, Eq. (43) and Theorem 2] The identity in Eq. (43), E_{\hat{G}}[\hat{G}(D^{-T})_{i,j}\hat{G}^H] = K Tr((D^{-T})_{i,j}) Σ(Φ), appears to contain an extra factor K. For \hat{G} = Σ^{1/2}N with N ∈ C^{N×K} having i.i.d. CN(0,1) entries, one has E[N A N^H] = Tr(A) I_N, so the correct result is Tr((D^{-T})_{i,j}) Σ(Φ), without the multiplicative K. This changes the expression in Theorem 2 and therefore affects the MSE comparison in Fig. 6. The authors should verify this calculation and correct Theorem 2 or the derivation leading to it.
minor comments (5)
  1. [Throughout] There are numerous typographical errors, including 'recpetion thermal noise' (Section 3.2), 'figital processor' (Section 3.1), 'anlod combining weights' (Section 4.2.4), 'expresion' (Proposition 1 proof), and 'estimation od the cascaded channel' (Fig. 7 caption). These should be corrected in a revision.
  2. [Section 4.2.1] The frame structure defines τ = B T and orthogonal pilots of length T, but the manuscript does not state the necessary condition T >= K for K orthogonal pilot sequences. Adding this constraint explicitly would clarify the scope of Proposition 1 and the numerical setup.
  3. [Section 4.2.4] The text says the UTs transmitted T = 70 pilot symbols, but it is unclear how B and T are chosen in relation to the frame structure and to the noise-free bound of Proposition 1. Please specify whether Fig. 6 uses B = 1, B > 1, and whether the numerical setup respects the T = K condition needed for Proposition 1.
  4. [Section 4.1.2] The phrase 'the (i,j)-th diagonal of the FIM' should likely read 'the (i,j)-th entry of the FIM'. Also, the notation C_n^{-1} is used before C_n is defined; please reorder for clarity.
  5. [Section 4.2.3, proof of Theorem 2] In the proof, the expression 'yBS[ℓ]' appears where 'yBS(b)' is intended. The internal numbering of Proposition 2 inside the proof of Theorem 2 is also confusing; consider presenting it as a separate lemma with a clear statement.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the derivation chain is self-contained from the assumed HRIS model.

full rationale

No significant circularity. The quantitative results (Proposition 1 pilot bound, Theorem 1 MMSE for G, and Theorem 2 MMSE bound for HBR) are derived algebraically from the stated observation model in Eqs. (9)-(14) using standard vec/Kronecker identities and linear MMSE theory; no parameter is fitted to data and no target result is assumed as an input. The power-splitting model in Eqs. (1)-(5) is an explicit modeling ansatz rather than a consequence of the later estimation claims. Self-citations such as [30] and [31] support hardware feasibility and prior implementations, not the mathematical conclusions, and they are published externally falsifiable results rather than unverified axioms in the derivation. The proof of Proposition 1 does contain a rigor gap: full column rank is argued only by row counts, which ignores the T >= K frame constraint (the skeptic's T>K rank argument). That is a correctness concern, not a circular reduction, because the claimed pilot bound is not equivalent to its assumptions by construction.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The chapter's analytical results are derived from a compact hardware abstraction (power splitting plus phase shifts) inherited from [30] and [31], combined with standard linear MMSE and rank arguments. No parameters are fitted to experimental data; rho, psi, and phi are controllable design variables swept in simulation. The main fragility is that the hardware abstraction is unvalidated beyond a single full-wave study, and the ISAC section contains internal equation inconsistencies.

assumptions (5)
  • domain assumption Each HRIS meta-atom splits incident power with ratio rho_l and applies independent phase shifts to reflected and sensed paths (Eqs. 1-2).
    This is the core hardware abstraction inherited from [30]; if the physical meta-atom cannot realize independent phase control for reflection and sensing, the entire model fails.
  • domain assumption The HRIS knows the BS position and can eliminate the static component of H_RB in the ISAC model.
    Stated in Section 4.1.1; used to define M and the FIM.
  • domain assumption Channel statistics R_G are perfectly known at the HRIS.
    Assumed in Theorem 1 proof; R_G = (sum g_k) I_N, requiring second-order statistics.
  • domain assumption All channels follow i.i.d. Rayleigh fading with known path-loss variances.
    Used throughout Section 4.2.1 for the noise-free and noisy MSE results.
  • standard math Linear MMSE estimation is optimal for Gaussian channels and is used to derive Theorems 1-2.
    Standard result from estimation theory [71], [72].

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Cite this review

Pith. "Pith review of Hybrid RISs for Simultaneous Tunable Reflections and Sensing." pith.science (2026). https://pith.science/paper/VGZF36RG

@misc{pith2026250716550,
  author       = {Pith},
  title        = {Pith review of: Hybrid RISs for Simultaneous Tunable Reflections and Sensing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VGZF36RG}},
  note         = {Machine review of arXiv:2507.16550}
}
read the original abstract

The concept of smart wireless environments envisions dynamic programmable propagation of information-bearing signals through the deployment of Reconfigurable Intelligent Surfaces (RISs). Typical RIS implementations include metasurfaces with passive unit elements capable to reflect their incident waves in controllable ways. However, this solely reflective operation induces significant challenges in the RIS orchestration from the wireless network. For example, channel estimation, which is essential for coherent RIS-empowered wireless communications, is quite challenging with the available solely reflecting RIS designs. This chapter reviews the emerging concept of Hybrid Reflecting and Sensing RISs (HRISs), which enables metasurfaces to reflect the impinging signal in a controllable manner, while simultaneously sensing a portion of it. The sensing capability of HRISs facilitates various network management functionalities, including channel parameter estimation and localization, while, most importantly, giving rise to computationally autonomous and self-configuring RISs. The implementation details of HRISs are first presented, which are then followed by a convenient mathematical model for characterizing their dual functionality. Then, two indicative applications of HRISs are discussed, one for simultaneous communications and sensing and another that showcases their usefulness for estimating the individual channels in the uplink of a multi-user HRIS-empowered communication system. For both of these applications, performance evaluation results are included validating the role of HRISs for sensing as well as integrated sensing and communications.

Figures

Figures reproduced from arXiv: 2507.16550 by the authors.

Figure 1
Figure 1. Illustration of an HRIS and the constitutive hybrid meta-atom design proposed in [30]. The layers of the meta-atom on the top right are artificially separated for better visualization. The cross section of the metasurface, with a focus on a single meta-atom, and the coupled wave signal path are depicted on the bottom right drawing. between near-perfect absorption and reflection. In this case, the metasurface has two… view at source ↗
Figure 2
Figure 2. The uplink of a two-user MIMO system incorporating the proposed HRIS that also senses a portion of the impinging UT signals, along with the operation model of the HRIS. In this model, the HRIS consists of 𝑁 hybrid meta-atoms. The incident signal at each meta-atom is split into a portion which is reflected (after tunable phase shifting), while the remainder of the signal is sensed and processed locally by a baseband … view at source ↗
Figure 3
Figure 3. A simple model for the HRIS operation. The parameter [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: The trade-off between achievable downlink rate and localization coverage performance for a setup including an HRIS with 𝑁 = 256 meta-atoms and a BS with 𝑀 = 16 antenna elements, considering 𝑇¯ = 100 symbol transmissions. of 28 GHz, where coherent channel blocks span 𝑇¯…
Figure 2
Figure 2. Figure 2: Let us assume that there is no direct link between the BS and any of the [PITH_FULL_IMAGE:figures/full_fig_p014_2.png]
Figure 5
Figure 5. Figure 5: The frame structure for estimating the individual HRIS-UTs and BS-HRIS [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]
Figure 6
Figure 6. Figure 6: Normalized MSE performance in recovering the combined HRIS-UTs channel G at the HRIS side and the BS-HRIS channel HBR at the BS, considering 30 dB transmit SNR as well as different power splitting values 𝜌 and phase configurations. ventional estimation of the cascaded …
Figure 7
Figure 7. Figure 7: Normalized MSE performance of the estimation od the cascaded channel as a function of the number 𝑁𝑟 of the reception RF chains at the HRIS for two different transmit SNR values in dB. The performance using a purely reflective RIS via the scheme of [44] is also shown. p…

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    Holographic MIMO communications: Theoretical foundations, enabling technologies, and future directions,

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    Reconfigurable intelligent surfaces and metamaterials: The potential of wave propagation control for 6G wireless communications,

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    G. C. Alexandropoulos, I. Vinieratou, and H. Wymeersch, “Localization via multiple reconfig- urable intelligent surfaces equipped with single receive RF chains,”IEEE Wireless Commun. Lett., vol. 11, no. 5, pp. 1072–1076, 2022

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    3D local- ization with a single partially-connected receiving RIS: Positioning error analysis and algorithmic design,

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    I. Gavras and G. C. Alexandropoulos, “Simultaneous communications and sensing with hybrid reconfigurable intelligent surfaces,” inProc. European Conf. Antennas Propag., Stockholm, Swe- den, 2025

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    Graph-CNNs for RF imaging: Learning the electric field integral equations,

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    T. Q. Duong, V. N. Q. Bao, H. Tran, G. C. Alexandropoulos, , and H. Zepernick, “Effect of primary networks on the performance of spectrum sharing AF relaying,”Electron. Lett., vol. 48, no. 1, pp. 25–27, 2012

    2012

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    G. C. Alexandropoulos, A. Papadogiannis, and P. C. Sofotasios, “Outage performance of cognitive cooperative networks with relay selection over double-rayleigh fading channels,” HINDAWI J. Computer Netw. Commun., vol. 2011, Article ID 560528, 2011

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    G. C. Alexandropoulos, M. A. Islam, and B. Smida, “Full-duplex massive multiple-input, multiple- output architectures: Recent advances, applications, and future directions,” IEEE Veh. Technol. Mag., vol. 17, no. 4, pp. 83–91, 2022

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    P. Gavriilidis, I. Atzeni, and G. C. Alexandropoulos, “Metasurface-based receivers with 1-bit ADCs for multi-user uplink communications,” in Proc. IEEE Int. Conf. Acoustics, Speech Signal Process, Seoul, South Korea, 2024, pp. 9141–9145

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.