RIS-Aided Integrated Sensing and Communication Systems under Dual-polarized Channels

Cunhua Pan; Dongnan Xia; Hong Ren; Jiangzhou Wang; Yasheng Jin; Zhiyuan Yu

REVIEW 2 major objections 6 minor 45 references

RIS-Aided Integrated Sensing and Communication Systems under Dual-polarized Channels

T0 review · 2 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Using dual-polarized transmit arrays and a dual-polarized RIS can roughly double the sum rate of an integrated sensing and communication system at the same physical array size, while exploiting the two polarizations to track two targets…

desk verdict First DP-RIS-ISAC design, but the headline polarization gain is inflated by an unphysical RIS model that lets each element amplify by up to 6 dB. read the letter →

arxiv 2501.00909 v1 pith:W7NSOMYY submitted 2025-01-01 cs.IT eess.SPmath.IT

Dongnan Xia , Cunhua Pan , Hong Ren , Zhiyuan Yu , Yasheng Jin , Jiangzhou Wang This is my paper · ORCID

classification cs.ITeess.SPmath.IT

keywords integratedsensingandcommunicationdual-polarizedchannelsreconfigurableintelligentsurfacesumratemaximizationbeamformingmajorization-minimizationweightedminimummean-squareerrorpolarizationdiversity

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 paper proposes an integrated sensing and communication (ISAC) system in which the base station, the reconfigurable intelligent surface (RIS), and the users all use dual-polarized (DP) antennas, and argues that exploiting the polarization domain materially improves performance. The system transmits vertical and horizontal signals simultaneously, using the two polarizations both to serve users and to illuminate two different radar targets, and jointly optimizes the DP transmit beamforming matrix and the DP RIS phase-shift matrix to maximize the sum rate subject to per-target sensing signal-to-noise-ratio constraints. The authors solve the non-convex problem by a WMMSE reformulation, alternating optimization, a majorization-minimization step for the unit-modulus RIS phases, and a penalty-based closed-form beamforming update. Simulations show the DP configuration achieves roughly double the sum rate of a single-polarized system with the same physical array size (about 26 vs 15 nat/s/Hz at ten transmit elements) while maintaining both sensing beams, and they quantify a trade-off between sensing quality and communication rate. The paper's central claim is that polarization is a free and substantial resource for ISAC: it doubles the effective array size without doubling the footprint.

What carries the argument

The load-bearing object is the polarization-domain decomposition of the transmit signal: the DP signal $x = \sum_{k=1}^K F_k s_k$ is split by the selection matrices $E_1 = [I_{N_t} \, 0_{N_t}]$ and $E_2 = [0_{N_t} \, I_{N_t}]$ into vertical and horizontal parts, so that target 1 is illuminated only by $x_1 = E_1 x$ and target 2 only by $x_2 = E_2 x$. The DP channel is parameterised by an XPD factor $\beta$ that sets the variance ratio between co-polar and cross-polar components, and the DP RIS is represented by a $2L \times 2L$ phase-shift matrix $\Phi$ whose diagonal blocks $\Phi_{vv}$, $\Phi_{hh}$ and off-diagonal blocks $\Phi_{vh}$, $\Phi_{hv}$ model polarization-preserving and polarization-converting reflection. The optimization machinery is a WMMSE reformulation of sum rate into a weighted MSE objective, an alternating optimization that separates the beamforming update from the RIS update, a majorization-minimization step with closed-form phase update $\phi^{[t+1]} = e^{j \arg(q^{[t]})}$ for the unit-modulus constraint, and a penalty-based beamforming algorithm whose inner subproblems have closed-form solutions $F_k = A_k^{-1} b_k$, $X_k^{\mathrm{opt}} = F_k/(1+\tau)$, and $Y_k^{\mathrm{opt}} = V_1 F_k/(1-\mu_1)$. This combination makes the joint non-convex problem tractable and lets the DP gain appear in simulation.

What would settle it

Re-run the optimization with a target response matrix containing non-zero cross-polarization entries, then check whether both sensing constraints $\gamma_1 \geq \gamma_{1,\mathrm{th}}$ and $\gamma_2 \geq \gamma_{2,\mathrm{th}}$ can still be met while the sum-rate gain over single polarization persists; if the cross-polarized echo terms cause the two sensing constraints to interfere severely, the claimed dual-target advantage shrinks or disappears.

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Extended reading notes

Core claim

The paper's central claim is that a dual-polarized architecture—DP base station, DP RIS, and DP user antennas—substantially outperforms an otherwise identical single-polarized ISAC system in achievable sum rate, while enabling simultaneous dual-target sensing by assigning one polarization to each target. The sensing model treats vertical and horizontal transmit components independently: target 1 is illuminated only by the vertical part $x_1 = E_1 x$ and target 2 only by the horizontal part $x_2 = E_2 x$, with rank-one target response matrices $A_1$ and $A_2$. Under this model, the joint optimization of the beamforming matrices $\{F_k\}$ and the RIS phase-shift matrix $\Phi$ yields a beampattern in which the vertical and horizontal beams point at the two targets while the communication sum rate is maximized. The numerical results show the DP system scaling from 14 to 26 nat/s/Hz as the number of transmit elements grows from 4 to 10, versus 11 to 15 for a single-polarized system of the same size; an SP system with twice the physical size reaches 23 to 31 nat/s/Hz, slightly higher, confirming that DP recovers most of the gain of doubling the array without the extra footprint.

Load-bearing premise

The sensing model assumes each target reflects only the polarization aimed at it and never mixes vertical with horizontal; real targets depolarize, so this assumption carries the dual-target advantage.

Editorial extensions

If this is right

At fixed physical array size, switching from single polarization to dual polarization roughly doubles the achievable sum rate (about 14 to 26 nat/s/Hz versus 11 to 15 nat/s/Hz as the transmit array grows from 4 to 10 elements) while preserving dual-target sensing.
The two orthogonal polarizations can act as separate sensing beams pointed at two different targets, giving concurrent multi-target detection and polarimetric target information without adding antennas.
Higher XPD, meaning cleaner polarization preservation, raises the sum rate up to a saturation point, and the gain is larger when the RIS has more elements.
There is a tunable trade-off between sensing and communication: raising the required sensing SNR lowers the communication sum rate, and more transmit antennas make the system less sensitive to that trade-off.
With only a few phase-quantization bits, roughly four or more, the DP system already nearly reaches its continuous-phase sum rate, so the dual-polarization gain survives practical RIS hardware.

Reading between the lines

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

The paper leaves implicit that a depolarizing target would couple the two sensing constraints; a direct extension is to add cross-polarized echo terms to $\gamma_1$ and $\gamma_2$ and optimize a joint sensing constraint instead of two decoupled ones.
Because the DP system nearly matches an SP system with twice the physical aperture, the results imply a cost argument the paper does not state: at high frequencies where array footprint is tight, DP hardware may be a cheaper way to buy degrees of freedom than doubling the element count.
The polarization split suggests a sensing multiple-access scheme: with additional orthogonal bases such as $\pm45^\circ$ slanted or circular polarizations, more than two targets could be illuminated simultaneously, although the rank-one target model would need to be generalized.
The penalty-based closed-form beamforming update is largely independent of the specific sensing SNR thresholds, so the same algorithm could be adapted to other ISAC metrics, for example maximizing sensing mutual information under a sum-rate constraint, by swapping only the penalty terms.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

First paper I've seen that combines dual-polarized BS, RIS, and users in ISAC, and the WMMSE/AO/MM/penalty machinery is competently applied. The problem is the DP RIS model. Constraint (24e) makes the four polarization-path coefficients per element independently unit-modulus. A physical passive element has a 2x2 scattering matrix bounded by passivity; letting all four entries be 1 gives a largest singular value of 2, i.e., 4x reflected power. The MM update coherently aligns phases to exploit this, and because the RIS link dominates (path-loss exponents 2.25+2.25 vs 4.75 direct), part of the Fig. 5 gap (DP 14-26 vs SP 1x 11-15) is an artifact. The SP baselines use the standard |phi|=1 model, so the comparison is asymmetric. This is load-bearing, not a nit. The sensing model is also idealized: (16)-(18) give target 1 only the vertical component and target 2 only the horizontal, with no cross-polarization scattering. That assumption is structural to the dual-target sensing claim, and real targets depolarize.

Credit where due: the system model is new relative to the cited literature, the derivations are standard but carefully done, and the simulations show the expected qualitative trends for XPD, sensing thresholds, and quantization. Complexity analysis is reasonable. There are a few typos and sloppy definitions (e.g., 'KX k=i' in Eq. 35, F versus Fk in Section IV.D), but those are minor.

Bottom line: the idea is worth engaging and the algorithmic framework is reusable, but the performance claims need major revision. I'd send this to peer review with a request to impose a passivity constraint on the DP RIS element scattering matrix, relax or justify the polarization-isolated sensing model, and add Monte Carlo details or code. It's a candidate for a reading group discussion on model idealizations in RIS/ISAC.

Referee Report

2 major / 6 minor

Summary. This paper studies a dual-polarized RIS-aided ISAC downlink in which a DP BS illuminates two radar targets with orthogonal polarizations while serving K DP users through a direct and a RIS-reflected path. The authors maximize the achievable sum rate subject to two sensing SNR constraints and a unit-modulus constraint on the DP RIS phase matrices. The solution uses a WMMSE reformulation, alternating optimization, an MM algorithm for the RIS phases, and a penalty-based closed-form beamforming update; simulations compare DP with SP baselines, quantized phase shifts, XPD, and sensing-SNR trade-offs. The algorithmic machinery is standard and appears internally consistent, but the physical model of the DP RIS and the target scattering model contain idealizations that directly affect the headline DP-vs-SP conclusions.

Significance. If the idealizations were removed or justified, the paper would provide a useful complete framework for a DP ISAC design. It correctly identifies the polarization dimension as a way to double aperture capacity without increasing footprint, and it gives a transparent optimization pipeline with convergence checks and quantitative comparisons, including quantization and XPD sensitivity. The penalty/MM derivations are standard and the empirical convergence plots support the internal algorithmic claims. However, contribution 3 ('substantially outperforms traditional SP systems') is not established under a physically consistent model: the DP RIS constraint permits 6 dB per-element amplification, and the sensing model assumes perfect polarization selectivity at targets. These are not cosmetic: both are embedded in the problem formulation and the simulations. The paper is therefore promising but requires a substantive revision to the model before the central claims can be accepted.

major comments (2)

[Section II-C/IV-B, Eqs. (12), (24e), (46)] The DP RIS model violates passivity. Because the four block-diagonal phase matrices in (12) are optimized independently under |phi^pq_n|=1 for every {p,q}, the per-element 2x2 reflection matrix S_l is not constrained; S_l = [[1,1],[1,1]] is feasible and has sigma_max=2, so an incident wave with equal-power, in-phase v and h components is reflected with 4x (6 dB) the incident power. The MM update in Eq. (46) coherently aligns the four polarization paths, so the optimized Phi exploits this non-physical gain. With alpha_BS-RIS=alpha_RIS-UE=2.25 dominating the alpha_BS-UE=4.75 direct link, and with the SP baselines constrained to the passive single-coefficient |phi|=1 model, a material part of the DP-vs-SP sum-rate gap in Fig. 5 may be an artifact of the RIS model rather than of dual polarization. The authors should replace (24e) with a physically realizable per-element constraint (e.g., sigma_max(S_l)<=1 for passive surfaces, or a unitary/reciprocal scattering matrix for lossless reciprocal surfaces) and re-run the simulations.
[Section III-B, Eqs. (16)-(18) and constraints (24b)-(24c)] The sensing model assumes perfect polarization selectivity at the targets. Eq. (16) sets y1 = A1 x1 + z1 and y2 = A2 x2 + z2 with x1 = E1 x and x2 = E2 x, so target 1 is illuminated only by the vertical component and target 2 only by the horizontal component; the rank-one response matrices in (18) contain no cross-polar entries. Real targets are characterized by a 2x2 polarimetric scattering matrix and depolarize the incident wave, so the v-pol and h-pol sensing beams will couple. Since the sensing SNR constraints (24b)-(24c) and the dual-target beampattern claim in Fig. 9 are built directly on this assumption, the claimed polarization-domain multi-target sensing advantage is not established for general targets. The authors should either explicitly restrict the claim to ideal polarization-selective point targets and discuss the restriction, or extend the model to include target cross-polar scattering.

minor comments (6)

[Section IV-A, Eq. (25)] U_k is declared as C^{2x1}, but it must be C^{2x2} to estimate the 2x1 data vector d_k in (25); correct the dimension and the subsequent trace expressions.
[Section IV-B, Eq. (38)] F and C are 2Lx2L matrices, so their 2x2 blocks are LxL, not N_t x N_t as written; the same symbol F is also used for the transmit covariance matrix sum F_k F_k^H, which is confusing.
[Eq. (15) vs Section V] Eq. (15) uses log2, while Section V and Fig. 5 report rates in nat/s/Hz; make the log base and rate units consistent throughout.
[Eqs. (35)-(36)] Eq. (35) has a typo in the summation index ('k=i'), and Eq. (36) introduces N_k and H_{ru,k} without definition; please clean up the notation in the Phi-subproblem derivation.
[Algorithm 2 and surrounding text] The outer-loop continuation condition is described as the 'fractional increase' of (51), but (51) is a minimization objective; the criterion should be a decrease.
[Section V-B, Fig. 5] The SP 1x baseline uses N_t single-polarized elements while the DP system uses N_t dual-polarized elements, i.e., twice the number of RF/antenna ports in the same aperture; state explicitly that the 'same hardware scale' comparison is on physical aperture/element count, not number of ports, and note the power-per-antenna implications.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the sum-rate and sensing derivations are self-contained, and the DP-versus-SP comparison rests on an external channel model and standard WMMSE/MM equivalences.

full rationale

The paper's load-bearing claims are derived from a stated DP channel model, not from fitting a parameter to the quantity being predicted. The achievable sum rate in Eq. (15) and the sensing SNRs in Eqs. (22)-(23) are definitions and algebraic consequences of the model; neither is calibrated to simulation output. The WMMSE reformulation in Eqs. (27)-(30) is a standard equivalence, and the MM update in Eqs. (44)-(46) follows from a generic majorization inequality, not from a self-citation or an imported ansatz. The penalty-based beamforming solution in Section IV-C is obtained through KKT conditions and closed-form updates. The SP comparison baselines in Section V-B follow the external setup of [17] (Ozdogan and Bjornson), not the present authors' prior work, so the comparison is not built on a self-citation chain. While the DP advantage is a consequence of the chosen channel model—DP doubles the effective number of antennas and adds polarization paths—this is a modeling assumption rather than a circular derivation. The skeptic's concern that the DP RIS constraint (24e) permits non-passive amplification is a physical correctness issue, not a circularity of the derivation chain. No circular step can be exhibited, so the appropriate finding is no significant circularity.

Assumptions & free parameters 3 free parameters · 7 assumptions · 0 invented entities

No free parameters are fitted to the target results; the listed values are simulation inputs chosen from typical dual-polarized channel values. The strongest structural assumptions are the polarization purity assumptions: zero XPC in the channel and perfect polarization separation at the targets. These are domain idealizations rather than new physical entities.

free parameters (3)

XPD parameters beta (LoS and NLoS) = beta_LoS=0.1368 (8 dB), beta_NLoS=0.2403 (5 dB)
Set by hand from typical DP channel values; the central DP-vs-SP comparison uses these values, and Fig. 7 shows the advantage depends on them.
Rician factor omega = 2.5
Chosen in simulation setup (Section V); affects the LoS/NLoS balance in RIS-related channels and thus the achievable gains.
Sensing SNR thresholds gamma_1,th and gamma_2,th = varied 20-26 dB in Fig. 8
Chosen constraints to illustrate the sensing-communication tradeoff; they are not fit to data but are selected by the authors.

assumptions (7)

domain assumption Perfect CSI is available at the BS and receivers
Section III-A states 'Assuming perfect channel state information (CSI) is available at the BS.' This ignores channel estimation errors.
domain assumption Zero cross-polarization correlation (XPC=0) in DP channels
Eq. (7) asserts E{h_vv h*_hv}=0 etc., citing [43]; this makes the 2x2 polarization channel blocks statistically independent, simplifying the model.
domain assumption Direct BS-UE links are NLoS-only with i.i.d. Rayleigh fading
Section II-B: 'there is only non-line-of-sight (NLoS) link. Therefore, each element ... follows a zero-mean i.i.d. complex Gaussian distribution.' This ignores LoS and spatial correlation.
domain assumption BS-RIS and RIS-UE links follow Rician fading with rank-one LoS components
Eqs. (9), (11), (70); LoS components are constructed as outer products of array responses, a standard geometric model.
ad hoc to paper DP RIS has four independent unit-modulus phase-shift matrices
Eq. (12) and constraint (24e) allow arbitrary independent unit-modulus coefficients for the vv, vh, hv, hh polarization transitions; real passive RIS elements have coupled and power-constrained responses.
ad hoc to paper Targets are perfectly polarization-selective with rank-one point response matrices and no cross-polarization scattering
Eqs. (16)-(18): target 1 responds only to vertical component x1=E1x and target 2 only to horizontal x2=E2x, with A_i = a(theta_i)a^H(theta_i). This idealization is the main enabler of the claimed dual-target polarization multiplexing.
standard math WMMSE rate-MSE equivalence and MM surrogate minimization are valid
Used in Section IV-A and Lemma 1; standard results, not re-derived.

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Pith. "Pith review of RIS-Aided Integrated Sensing and Communication Systems under Dual-polarized Channels." pith.science (2026). https://pith.science/paper/W7NSOMYY

@misc{pith2026250100909,
  author       = {Pith},
  title        = {Pith review of: RIS-Aided Integrated Sensing and Communication Systems under Dual-polarized Channels},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/W7NSOMYY}},
  note         = {Machine review of arXiv:2501.00909}
}

read the original abstract

This paper considers reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) systems under dual-polarized (DP) channels. Unlike the existing ISAC systems, which ignored polarization of electromagnetic waves, this study adopts DP base station (BS) and DP RIS to serve users with a pair of DP antennas. The achievable sum rate is maximized through jointly optimizing the beamforming matrix at the DP BS, and the reflecting coefficients at the DP RIS. To address this problem, we first utilize the weighted minimum mean-square error (WMMSE) method to transform the objective function into a more tractable form, and then an alternating optimization (AO) method is employed to decouple the original problem into two subproblems. Due to the constant modulus constraint, the DP RIS reflection matrix optimization problem is addressed by the majorization-minimization (MM) method. For the DP beamforming matrix, we propose a penalty-based algorithm that can obtain a low-complexity closed-form solution. Simulation results validate the advantage of deploying DP transmit array and DP RIS in the considered ISAC systems.

Figures

Figures reproduced from arXiv: 2501.00909 by the authors.

**Figure 3.** Convergence behavior of the proposed algorithm [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

**Figure 4.** Convergence behavior of the constraints in Algorithm 2 [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗

Figures from the paper (4 more)

**Figure 2.** Figure 2: The simulation system scenario. A. Convergence of the proposed algorithm We present the convergence behavior of the proposed algorithm in [PITH_FULL_IMAGE:figures/full_fig_p010_2.png]

**Figure 5.** Figure 5: Comparison between SP and DP scenarios 2) SP 2x: To ensure fair comparison regarding the total system degrees of freedom, this configuration employs twice the number of DP transmit antennas and DP RIS elements, and each user is equipped with two SP antennas. For SP cha…

**Figure 7.** Figure 7: Sum rate versus the XPD of DP channel [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]

**Figure 8.** Figure 8: Sum rate versus different sensing SNR better absolute performance and higher sensitivity to XPD improvements, as shown by the steeper slope of the L = 40 curve. E. Impact of the sensing SNR on the sum rate [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]

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