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REVIEW 4 major objections 5 minor 1 cited by

Reconfigurable Intelligent Surface aided Integrated-Navigation-and-Communication in Urban Canyons: A Satellite Selection Approach

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

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Pith's one-line read The paper claims a single STAR-RIS can restore MEO satellite positioning in urban canyons by contributing one indirect link, and that indoor positioning cannot be achieved with one surface because all indirect measurements reduce to the…

desk verdict Indoor positioning is unobservable under the paper's own equations, and the abstract and body contradict each other; the outdoor urban-canyon idea is plausible but needs a refocused submission. read the letter →

arxiv 2507.01766 v1 pith:6ANFBPLS submitted 2025-07-02 cs.IT eess.SPmath.IT

Tianwei Hou , Da Guan , Xin Sun , Anna Li , Wenqiang Yi , Yuanwei Liu , Arumugam Nallanathan This is my paper
classification cs.ITeess.SPmath.IT
keywords STAR-RISintegratednavigationandcommunicationurbancanyonsatelliteselectionpositiondilutionofprecisionergodicrateNOMAMEO
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 claims that a STAR-RIS, a surface that can both reflect and transmit signals, can restore satellite navigation in urban canyons, where direct satellite-to-user lines of sight are blocked by buildings. The proposed network integrates navigation and communication on the same MEO satellite signal using superposition coding and successive interference cancellation, and it offers two satellite-selection rules: one that picks the blocked satellite that best improves positioning geometry, and one that picks the satellite giving the highest communication rate. The central result is that with three visible satellites, one STAR-RIS-provided indirect link is enough to complete outdoor positioning, while indoor users with no direct satellite links remain poorly localizable because all indirect measurements collapse to the same user-to-surface distance. The authors report that indoor positioning error stays above five meters no matter how many satellites are routed through the single surface.

What carries the argument

The key object is the STAR-RIS with two independent phase configurations, one reflection layer for outdoor users and one transmission layer for indoor users, together with the through-RIS pseudo-range equation $\rho_{cv} = r_{\tau,vR} + d_{Ru} + c\Delta t_r$, in which the user position enters only through the scalar distance $d_{Ru}$ to the surface. This equation feeds a least-squares geometry matrix $A$, and PDoP from $(A^TA)^{-1}$ guides the navigation-prioritized selection; the same surface's phase alignment, using the signal alignment technique, carries the communication-prioritized selection.

What would settle it

Simulate the least-squares position estimate for an indoor user with zero direct satellites and any number of satellites routed through one STAR-RIS, using the measurement model of Eq. (28). The matrix A from Eq. (38) will have identical first three columns for every via-RIS measurement, so $A^TA$ is singular; without an added prior or damping the estimated position error diverges. If the paper's finite indoor error is reproduced, it must come from an initialization or regularization step that should be identified in the model.

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Extended reading notes

Core claim

The paper establishes, on its own terms, that a single STAR-RIS array mounted on a building can turn a blocked MEO satellite into an additional navigation constraint for a user in an urban canyon. Combined with three directly visible satellites, the indirect measurement yields a solvable four-equation least-squares system for position and clock offset, and the navigation-prioritized algorithm selects the blocked satellite that minimizes position dilution of precision. For communication, the communication-prioritized algorithm selects the satellite with the best aggregated channel gain and achieves higher ergodic rates in the CO-INAC power-allocation regime. The paper also reports that indoor users, who see no direct satellites, suffer positioning errors that depend mainly on the distance to the single STAR-RIS and cannot be reduced below about five meters by adding more satellites through the same surface.

Load-bearing premise

Every via-STAR-RIS pseudo-range contains the user's position only as the scalar distance from the user to the single RIS, so for a user with no direct satellite links all rows of the least-squares geometry matrix are spatially identical; 3D position is unobservable and the reported finite indoor errors rely on initialization or regularization not stated in the model.

Editorial extensions

If this is right

  • An outdoor user with three visible satellites can obtain a complete positioning solution when the STAR-RIS adds one indirect satellite link; without the RIS, three satellites are insufficient and PDoP is undefined.
  • Navigation-prioritized selection prefers satellites that improve geometric diversity rather than the nearest ones, while communication-prioritized selection prefers the nearest satellite, creating a concrete navigation-communication trade-off.
  • Indoor users with zero direct links keep positioning errors dominated by the STAR-RIS-to-user distance even when many satellites are routed through the surface, so a single STAR-RIS cannot deliver indoor 3D positioning.
  • CO-INAC mode lets achievable ergodic rate grow with the number of RIS elements and shows a rate ceiling in the high-SNR regime, while NO-INAC supports only low-rate applications.

Reading between the lines

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

  • The rank argument implies that no number of satellites routed through one STAR-RIS can resolve an indoor user's 3D position, since all rows of the geometry matrix share the same spatial direction; fixing this would require multiple STAR-RISs at different locations, fusion with other sensors, or a regularized prior.
  • A testable extension is joint satellite-and-surface selection over several distributed STAR-RISs: the paper's PDoP criterion suggests that angular separation between surfaces, not just the number of links, is what improves geometry.
  • Because PDoP-based selection ignores received signal strength, a weighted PDoP or a joint rate-and-geometry objective would be a natural next step, since a geometrically favorable satellite may deliver too little power to be usable.
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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. This manuscript studies a STAR-RIS-aided MEO satellite integrated-navigation-and-communication (INAC) network that uses superposition coding and successive interference cancellation. It formulates pseudo-range equations for direct and STAR-RIS-relayed satellite links, computes position dilution of precision (PDoP), and proposes two satellite selection algorithms: NPA, which selects the relayed satellite minimizing PDoP, and CPA, which selects the satellite maximizing an achievable ergodic rate. Numerical results are presented for positioning error and achievable rate for both outdoor urban-canyon users and indoor users. The abstract states that a single STAR-RIS fails to serve indoor users, while the body and figures claim finite indoor positioning errors.

Significance. The topic is timely, and the architectural idea of using a STAR-RIS to create a virtual line-of-sight satellite link in an urban canyon is worth investigating. The paper also deserves credit for explicitly recording in Remark 1 and in the abstract that a single STAR-RIS provides only one virtual link, a limitation that the numerical section does not actually respect. If the stated model were correct, the demonstration that one relayed link plus three direct links suffices for outdoor positioning would be a useful example. However, the central navigation claim fails for the indoor scenario because the measurement model is structurally rank-deficient, and the rate analysis contains a summation error that invalidates the reported rate gains. No code or machine-checked derivations are provided, so all claims rest on the paper's own equations and figures.

major comments (4)
  1. [III-B, Eqs. (28), (38); V-A; Figs. 2 and 3] The indoor positioning result is contradicted by the paper's own equations. In Eq. (28), every through-RIS pseudo-range has the form r_tau,vR + d_Ru + c*Delta_t_r, where the user position appears only through the scalar distance d_Ru to the single STAR-RIS. Therefore, when several satellites are relayed through the same STAR-RIS, their rows in the geometry matrix A are identical in the spatial partial derivatives and have the same clock coefficient c. For an indoor user with zero direct satellites, as specified in Section V-A, every row of A is identical, so A^T A is singular, Eq. (35) is not invertible, and the PDoP in Eq. (43) is undefined. The finite indoor positioning errors plotted in Figs. 2 and 3 cannot be produced by the stated least-squares model in Eqs. (28)-(40) unless an unstated regularization, initialization, or pseudo-inverse is introduced. This is a structural observability failure, not a parameter-tuning issue: with one STAR-RIS, the relayed links carry only one scalar distance constraint on the user's position. The outdoor urban-canyon case with three direct satellites plus one relayed link may be full-rank and is not called into question by this specific defect, but the paper's headline claim explicitly covers both outdoor and indoor users, and the indoor component is unsupported as written. The manuscript itself concedes this in Remark 1 and in the abstract, yet Section V-A and Fig. 3 treat the indoor case as solvable.
  2. [IV-B, Eqs. (52)-(53), Figs. 4 and 6] The achievable ergodic rate expressions in Eqs. (52) and (53) are not correct summations over the N STAR-RIS elements. Both expressions sum over n but the summand in Eq. (52) is |beta_1,R g_R,1u h_R,i1 sqrt(L_Riu)| for every term, and Eq. (53) uses |beta_1,T g_T,1u h_R,i1 sqrt(L_Tiu)| for every term. If the index n is not present in the summand, the sum simply multiplies the first element's contribution by N; if the intent was to sum over elements, the summands should be beta_n,R g_R,nu h_R,in and beta_n,T g_T,nu h_R,in. As written, the expressions disregard all STAR-RIS elements except the first, which contradicts the phase-alignment design in Eqs. (48)-(51) and undermines the claims in Figs. 4 and 6 that adding STAR-RIS elements improves the achievable rate and reduces the required transmit power.
  3. [II-B, Eqs. (11)-(21), Figs. 6 and 7] The labels 'NO-INAC' and 'CO-INAC' appear to be reversed. Eq. (11) defines the NO-INAC case by omega_C > omega_N and states that the user first decodes the communication signal, treating the navigation signal as interference. That decoding order is communication-prioritized, not navigation-prioritized. The CO-INAC case in Section II-B.2 instead imposes omega_C < omega_N and decodes the navigation signal first, which is navigation-prioritized. This swap propagates through Eqs. (12)-(21), Remark 3, and the interpretations of Figs. 6 and 7. Since the power-allocation ordering determines which signal is decoded first under SIC, the reported rate comparisons cannot be interpreted as stated.
  4. [V-A, Remark 1, contribution list] The manuscript is internally inconsistent about the indoor case. The introduction's contribution list claims the proposed system provides 'revolutionary solutions in the urban canyon and indoor scenarios,' and Section V-A states that the STAR-RIS connects four satellites to the indoor user. However, Remark 1 states that a STAR-RIS array can only provide users with one virtual LoS link, and the abstract states that the single STAR-RIS fails to serve indoor users. These claims cannot all be true simultaneously. The contradiction is not merely editorial: the observability analysis in the first major comment shows that four relayed satellites through one STAR-RIS do not yield four independent positioning equations.
minor comments (5)
  1. [III-B, Eq. (29)] Eq. (29) defines r_tau,vR using the coordinates (x4, y4, z4), which is the fourth satellite, not the v-th satellite; the subscripts should be consistent with the index v used in Eq. (28).
  2. [II-A, Eqs. (6)-(7); V, parameter settings] The amplitude coefficient constraints are inconsistent. Eqs. (6)-(7) and the text state beta_n,R^2 + beta_n,T^2 = 1, but the simulations set both beta_n,R and beta_n,T to 0.5, which gives a squared sum of 0.5, a 3 dB energy inconsistency that should be corrected or explicitly justified.
  3. [Algorithm 1, Section IV-1] Algorithm 1 is not a well-defined pseudocode: it contains placeholder expressions such as 'F(bar_x) = bar_l' and 'x0 approximately = bar_x', and the objective in Eq. (45) uses the positioning error epsilon_u without specifying how it is computed from the measurement model. The algorithm should be replaced with a precise iterative least-squares description.
  4. [Remarks 2 and 4] Remarks 2 and 4 are presented as findings of the proposed algorithms, but they are essentially direct consequences of the geometry and path-loss expressions: PDoP improves with angular diversity, and the rate in Eqs. (52)-(53) decreases with distance through the large-scale fading factors. They should be stated as observations rather than as algorithm-derived results.
  5. [Fig. 8] The horizontal axis of Fig. 8 is labeled with numerical values followed by '107', which appears to be a typo for a scientific-notation multiplier; the axis label should be corrected.

Circularity Check

1 steps flagged · score 3.0 of 10

The PDoP-to-position-error relation in Eq. (43) is definitional, making Fig. 2's trend a construction rather than a prediction; the central outdoor positioning derivation remains independent.

  1. self definitional [Section III-C, Eq. (43), and Fig. 2]
    "It is clear that the measurement error is amplified by the weight coefficient matrix F and becomes the positioning error. ... PDoP = sqrt(sigma_x^2 + sigma_y^2 + sigma_z^2) = sqrt(h11 + h22 + h33)."

    By Eq. (42), Cov([epsilon_x epsilon_y epsilon_z epsilon_t]^T) = (A^T A)^{-1} sigma_URE^2 = F sigma_URE^2, and Eq. (43) defines PDoP as sqrt(h11+h22+h33). Therefore the RMS 3D positioning error equals sigma_URE * PDoP exactly by construction. Fig. 2's observation that positioning error increases with PDoP is not an independent numerical discovery but a restatement of this definitional scaling; the explanation that high PDoP indicates poorer geometry is the same identity. The outdoor full-rank A computation is still an independent geometric calculation, so only the presentation of the PDoP-versus-error curve as a predictive result is circular.

full rationale

The core derivation of the paper is not fitted to data. The pseudorange equations in (27)-(28), the least-squares geometry matrix A in (38), and the PDoP computation in (43) follow the standard satellite-positioning model, and the outdoor urban-canyon claim (three direct satellites plus one STAR-RIS-reflected satellite) is supported by a full-rank geometric construction that is independent of the paper's numerical choices. The STAR-RIS channel model and the NPA/CPA satellite-selection objectives are also stated as explicit models rather than as predictions drawn from the results. The main circular element is the PDoP-to-positioning-error relationship: positioning error is not predicted from PDoP but is, by Eq. (42)-(43), equal to sigma_URE times PDoP, so Fig. 2's monotonic curve is a tautological consequence of the definition. Other flagged aspects of the paper, such as the admitted failure of a single STAR-RIS for indoor scenarios and the rank-deficiency concern for zero-direct-satellite indoor users, are correctness or modeling-consistency issues rather than circularity: they do not reduce a claimed prediction to an input by construction. Self-citations, e.g., the phase-alignment reference [23], concern a standard technique and are not load-bearing in a way that forces the paper's conclusions. Overall, the central outdoor positioning derivation has independent content, but the definitional PDoP-error identity warrants a modest circularity score of 3.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The model relies on standard pseudorange least-squares and fading assumptions, plus the questionable assumption that several STAR-RIS-relayed satellite signals give independent positioning constraints. The latter is the load-bearing premise that fails for indoor users.

free parameters (4)
  • Power allocation factors ω_N, ω_C = ω_C=0.8, ω_N=0.2 (Fig. 6); ω_C=0.65, ω_N=0.35 (Fig. 4)
    Hand-chosen inputs that define the NO/CO regimes and directly set the rate and SNR trade-offs; no optimization or measurement justifies them.
  • Path loss exponents = 2, 2, 2.2 for satellite-RIS, satellite-user, RIS-user links
    Simulation inputs with no empirical calibration; they set the SNR and hence all quantitative rate and power results.
  • Shadowed Rician fading parameters = b=0.279, m=2, Ω=0.251
    Standard channel parameters taken from prior literature; they shape the Monte Carlo statistics but are not fitted here.
  • STAR-RIS amplitude coefficients = β_R=β_T=0.5
    Chosen for simplicity, but the same section states β_R^2+β_T^2=1; 0.25+0.25=0.5, so the setting is internally inconsistent.
assumptions (5)
  • domain assumption Pseudo-range measurement errors are independent zero-mean Gaussian with variance σ_URE^2.
    Eq. (41)-(42) use this to define PDoP; real GNSS errors include correlated multipath, especially in urban canyons.
  • domain assumption Code division lets users separate satellite signals, so a single INAC satellite can represent the communication payload.
    Section II.B states this simplification; it avoids modeling inter-satellite interference but is not used for positioning geometry.
  • ad hoc to paper Each STAR-RIS-relayed satellite contributes a distinct positioning equation of the form (28).
    This is the load-bearing premise: in fact all relayed equations depend on user position through the same RIS-user distance, so they are not independent for a single RIS.
  • domain assumption The STAR-RIS position and satellite positions are known exactly.
    Eq. (29) and the positioning equations require exact coordinates; any error in Table I positions would shift the solution.
  • domain assumption Shadowed Rician/Rician small-scale fading models describe the satellite-RIS and RIS-user links.
    Section II.A; these are standard but unvalidated for MEO signals through a building-mounted RIS.

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Cite this review

Pith. "Pith review of Reconfigurable Intelligent Surface aided Integrated-Navigation-and-Communication in Urban Canyons: A Satellite Selection Approach." pith.science (2026). https://pith.science/paper/6ANFBPLS

@misc{pith2026250701766,
  author       = {Pith},
  title        = {Pith review of: Reconfigurable Intelligent Surface aided Integrated-Navigation-and-Communication in Urban Canyons: A Satellite Selection Approach},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6ANFBPLS}},
  note         = {Machine review of arXiv:2507.01766}
}
read the original abstract

This study investigates the application of a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided medium-Earth-orbit (MEO) satellite network for providing both global positioning services and communication services in the urban canyons, where the direct satellite-user links are obstructed. Superposition coding (SC) and successive interference cancellation (SIC) techniques are utilized for the integrated navigation and communication (INAC) networks, and the composed navigation and communication signals are reflected or transmitted to ground users or indoor users located in urban canyons. To meet diverse application needs, navigation-oriented (NO)-INAC and communication-oriented (CO)-INAC have been developed, each tailored according to distinct power allocation factors. We then proposed two algorithms, namely navigation-prioritized-algorithm (NPA) and communication-prioritized-algorithm (CPA), to improve the navigation or communication performance by selecting the satellite with the optimized position dilution of precision (PDoP) or with the best channel gain. The effectiveness of the proposed STAR-RIS-aided INAC network is quantified by analyzing the positioning error for navigation services and by evaluating communication performance through achievable ergodic rate metrics. Our satellite selection approach indicates that: the positioning services at the urban canyon users can be completed with the aid of STAR-RIS. 2) Additionally, it is observed that while a single STAR-RIS array can extend the navigational link, it fails to serve users in indoor scenarios, highlighting a limitation in the current system design.

Figures

Figures reproduced from arXiv: 2507.01766 by the authors.

Figure 1
Figure 1. The illustration of the STAR-RIS-NOMA-assisted [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. The positioning error versus PDoP of both outdoor [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 4
Figure 4. The minimum transmit power versus the number of [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figures from the paper (4 more)
Figure 3
Figure 3. Figure 3: The positioning error versus the number of satellite [PITH_FULL_IMAGE:figures/full_fig_p009_3.png]
Figure 5
Figure 5. Figure 5: The PDoP versus the number of directly connected [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: The achievable ergodic rate of the indoor and outdoor [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 8
Figure 8. Figure 8: The comparison between positioning error and [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]

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