Performance Analysis of RIS-Aided High-Mobility Wireless Systems

Chau Yuen; Hanwen Hu; Jiancheng An; Lu Gan

REVIEW 3 major objections 4 minor 15 references

Performance Analysis of RIS-Aided High-Mobility Wireless Systems

T0 review · 3 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read Jointly optimizing RIS phases and transmit beamforming gives a high-speed train downlink 15 dB more channel gain and zero outage probability.

desk verdict The outage-elimination claim is contradicted by the paper's own non-central chi-square model, and the 'optimal' phase design is a heuristic; otherwise a routine RIS-HST extension. read the letter →

arxiv 2508.15375 v1 pith:EWQFP3N6 submitted 2025-08-21 eess.SP

Hanwen Hu , Jiancheng An , Lu Gan , Chau Yuen This is my paper · ORCID

classification eess.SP

keywords reconfigurableintelligentsurfacehigh-speedtraincommunicationsMISObeamformingblockcoordinatedescentchannelgainoutageprobabilityDopplershiftRicianfading

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 tries to show that adding a reconfigurable intelligent surface to a high-speed train downlink, and tuning it together with the base station's beamformer, turns a channel that suffers Doppler shift and fast fading into one with consistently high gain. It proposes a block coordinate descent algorithm that alternates between optimizing the RIS phase shifts and the transmit beamforming vector, and reports an average channel gain improvement of 15 dB over fixed-phase or no-RIS baselines. In the simulated setting, this gain is enough to drive outage probability to zero and to improve rate, capacity, and bit error rate. If the result holds, it would make a passive surface a practical way to solve the central wireless problem of high mobility.

What carries the argument

A block coordinate descent loop that alternates between two blocks: the RIS phase-shift matrix and the transmit beamforming vector. The phase block is solved by using the structure of the line-of-sight path: the phase vector is chosen to cancel the Doppler phase e^{j2πk f_d T_c} and align with G w_k, and then a single phase rotation e^{jε_k} aligns the composite reflected path with the direct path. The beamforming block is the standard maximum-ratio transmission along the equivalent channel h_{r,k}^H Φ_k G + h_{d,k}^H. Together the two steps target the channel-gain objective F(w_k, Φ_k) in problem P1.

What would settle it

Simulate the same high-speed MISO link at Rician factor 3 with 1600 elements, and for a fixed beamformer compare the paper's line-of-sight-only phase update against a phase vector found by coordinate ascent or a direct search over phases; a measurable gain increase would falsify the claimed optimality. Separately, compute the outage count from the empirical distribution of |h_hat_k|^2 instead of the Gaussian approximation in Eqs. (22)-(23) and check whether outage remains exactly zero across all time slots.

Watch

Extended reading notes

Core claim

The central claim is that in a Rician high-speed train MISO downlink, the joint optimization of RIS phases and an MRT beamformer—not the beamformer alone—unlocks most of the performance. The proposed BCD algorithm averages 15 dB higher channel gain than MRT with random or zero RIS phases, and with 1600 RIS elements the optimized RIS eliminates outage probability at a 10 dB SNR threshold. The mechanism is a two-step phase update: first the RIS phases cancel the Doppler phase on the line-of-sight component of the reflected channel and beamform that component toward the receiver, then a common scalar rotation aligns the reflected signal with the direct signal; the beamformer is then updated as

Load-bearing premise

The load-bearing premise is that ignoring the non-line-of-sight part when setting the RIS phases, and then treating the whole optimized channel as one complex Gaussian, does not change the outcome; if either simplification is wrong, the 15 dB and zero-outage claims lose their support.

Editorial extensions

If this is right

At the simulated parameters (1600 RIS elements, Rician factor 3, 360 km/h), the optimized RIS removes outage entirely rather than merely reducing it against a 10 dB SNR threshold.
Achievable rate grows with the number of RIS elements for the RIS-aided link, while the no-RIS baseline stays flat in the number of elements.
Channel capacity improves at every time slot, by up to about 1.5 kbps, suggesting the RIS suppresses the time-varying loss of the direct path.
Bit error rate first worsens and then improves as the train moves from the base station toward the RIS, meaning the reflected path substitutes for the direct path.
The 15 dB average gain is measured against schemes that use MRT without optimizing the RIS phase, so phase tuning itself is what carries most of the reported improvement.

Reading between the lines

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

In my reading, the claimed optimality of the phase update stops after aligning the line-of-sight component; with Rician factor 3, the non-line-of-sight component still carries noticeable variance, so a phase update that also matches those parts should yield additional gain beyond the reported 15 dB.
The zero-outage result is tied to the chosen operating point: smaller RIS arrays, lower Rician factors, or thresholds above 10 dB should bring outage back, and the paper's own Gaussian approximation is the first place to test that.
A natural extension the paper does not pursue is applying the same BCD idea with discrete per-element phase constraints and imperfect channel estimates; the 15 dB and outage-elimination numbers would need recalibration under those constraints.

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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

Honestly, this one has a good setting but the main results don't survive contact with its own equations. The paper is a straightforward RIS phase-alignment plus MRT for high-speed train MISO, and the simulations show a 15 dB gain over their baselines, which is plausible. But the two headline claims—'optimal' phase design and 'eliminates outage'—are both overreach. The phase update in Section III-A aligns only the LOS component and then applies a common rotation; with κ=3 the NLOS component is not negligible, so this is a heuristic, not the maximizer of P1. The outage analysis is worse: Eq. (22)–(23) assert the composite channel after phase adjustment is Gaussian, but the phase in Eq. (17) is a function of the random channel, so the distribution isn't circular Gaussian. Even granting the Gaussian model, Eq. (24) with a finite non-centrality parameter gives positive outage for any positive threshold; Figure 3(b) shows exactly zero, which is internally inconsistent. That's not a minor caveat—'outage eliminated' is in the abstract and conclusion. The paper also doesn't benchmark against prior RIS-HST algorithms, only random phase and no RIS, so the novelty is incremental. On the plus side, the system model is clearly laid out, the BCD algorithm is easy to follow, and the simulation setup is transparent enough to reproduce. This could be a useful reference for RIS-HST modeling if the claims were trimmed. As it stands, the paper needs major revision: retract or soften the outage claim, rename the phase design a heuristic, and add comparisons to existing methods. I'd send it to peer review—the topic is active and the errors are instructive—but I'd expect rejection or major overhaul. Not something I'd cite in its current form.

Referee Report

3 major / 4 minor

Summary. The paper studies a RIS-assisted high-mobility HST MISO downlink. It formulates a channel-gain maximization problem P1 and proposes a BCD algorithm that alternates between a RIS phase update (LOS Doppler cancellation plus a scalar phase rotation) and MRT transmit beamforming. It then presents analytical expressions for achievable rate, channel capacity, outage probability, and BER, and reports simulations claiming an average 15 dB channel gain improvement over baselines and that the RIS 'eliminates' outage probability.

Significance. The problem is timely and the system model is representative of current RIS-railway studies. If the optimization were actually optimal and the outage model were correct, the claimed 15 dB gain and outage elimination would be noteworthy. A positive feature is that the simulation setup is self-contained and uses explicit parameters; no machine-checked proofs or reproducible code are provided. However, the central technical claims are not supported: the proposed phase update is not the maximizer of the phase subproblem, and the outage analysis is internally inconsistent with the paper's own distributional model. The headline results therefore cannot be accepted as stated.

major comments (3)

[Section III-A, Eqs. (13)-(17)] The proposed phase update does not solve the phase subproblem of P1. Equation (13) aligns only the LOS component of the cascaded channel; the NLOS term in Eq. (14), sqrt(1/(1+kappa)) h_NLOS,k Phi_k G w_k, is left uncontrolled and depends on the RIS phases through h_NLOS,k^H Phi_k G w_k. With kappa=3, the NLOS weight 1/(1+kappa)=0.25 is not negligible. The subsequent scalar rotation e^{j epsilon*} in Eq. (17) cannot compensate for per-element phase mismatches in the NLOS component. Thus Phi_k^* is not optimal, Algorithm 1's monotonicity/convergence is not established, and the labels 'optimal phase'/'optimal beamforming and phase' in Figs. 2-3 are unsupported. The 15 dB gain claim may be an artifact of this incomplete optimization rather than a true optimum.
[Section IV-C, Eqs. (21)-(24)] The outage-elimination claim is mathematically impossible under the paper's own model. With hat_h_k ~ CN(mu_h,k, sigma^2_h,k) and sigma^2_h,k > 0 (as given by Eq. (23) for kappa=3), |hat_h_k|^2 has a non-central chi-square distribution with 2 degrees of freedom, whose CDF is strictly positive at any finite positive threshold. Since gamma_{0,k} = sigma^2 gamma_th / sigma^2_h,k > 0 for gamma_th=10 dB, Eq. (24) gives P_out,k > 0. Figure 3(b) shows P_out=0 for all time slots, contradicting Eq. (24). The Abstract and Section V claim that the RIS 'eliminates outage probability' is therefore false under the paper's own distributional assumptions.
[Section IV-C, Eqs. (22)-(23)] The Gaussian model for hat_h_k is not derived and is generally incorrect. The phase e^{j epsilon*} in Eq. (17) depends on the random realizations of h_{k,(1)} and h_d,k^H w_k; multiplying the Gaussian NLOS component by this data-dependent phase breaks circular symmetry, so hat_h_k is not CN(mu_h,k, sigma^2_h,k). Moreover, Eq. (22) treats h_d,k^H w_k as a mean, while Eq. (23) uses |h_d,k^H w_k|^2 as a variance, but h_d,k^H w_k contains a deterministic LOS part. The quantity in Eq. (23) is the squared magnitude of a deterministic-plus-random quantity, not the variance of the composite channel. Thus Eq. (24) does not follow, and the simulated outage curve in Fig. 3(b) is not a consequence of the stated formulas.

minor comments (4)

[Throughout] Typographical errors should be corrected: 'Achivable Rate' in Section IV-A and Fig. 2(b); 'Outrage probability' in Fig. 3(b); 'chanel gain' in Eq. (9); 'dictance' in Section V; 'coverage probability' should read 'outage probability' in the paragraph after Eq. (23).
[Section III-A, Eqs. (11)-(13)] The notation is inconsistent: v_k is introduced as the vector of RIS phases, but Eq. (13) suddenly uses v_{k,(1)} without formally defining it. Also, the relationship between diag(h_LOS^H) G w_k in Eq. (12) and diag(a_y(theta_2, phi_2) circle a_z(phi_2)) G w_k in Eq. (13) should be stated explicitly.
[Section IV-C, Eqs. (22)-(23)] The symbols rho and varrho are visually similar and used with different powers; using the explicit factors kappa/(1+kappa) and 1/(1+kappa) would improve clarity and avoid confusion in the variance formula.
[Section V] The simulation section does not report Monte-Carlo repetitions, confidence intervals, or a comparison with an exhaustive/brute-force phase search for small N. Given that optimality is at issue, such a validation would be necessary to support the 15 dB gain claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: simulations are self-contained and no prediction reduces to an input.

full rationale

The paper's derivation chain is self-contained. The system model defines the direct and cascaded Rician channels, the optimization problem P1, and the BCD updates in Eqs. (13)-(18). The phase-shift update uses the triangle inequality to fix a scalar rotation, and the beamforming update uses standard MRT; neither is a restatement of the reported conclusions. The 15 dB channel-gain improvement and outage results in Section V are obtained from Monte Carlo/analytic evaluation of the model, not from any parameter fitted to match a target. The paper cites prior work by the authors (refs. [6], [9]-[12]) only as background and system-model context; no load-bearing claim depends on those citations. Concerns about the outage-elimination claim under the paper's own non-central chi-square model (Eqs. 22-24) and the unjustified Gaussianity of h_k are internal-model consistency and correctness issues, not circularity: the derivation does not reduce a prediction to its input by construction. Under the requested rubric, no circular step is exhibitable from the text.

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

The paper's results rest on a small number of scenario choices (κ, path loss exponents) and on the key modeling assumption that the NLOS cascaded component can be ignored in the phase update. No new physical entities are introduced.

assumptions (4)

ad hoc to paper The RIS phase update may ignore the NLOS component of the cascaded channel and still be called optimal
Section III-A derives Φ* by aligning only the LOS component; the NLOS part is not aligned per element, which is not the maximizer of P1.
domain assumption Perfect CSI of all links is available for each time slot
The optimization in (P1) and Algorithm 1 assume exact knowledge of h_d,k, h_r,k, G, and the Doppler frequency; no estimation overhead is considered.
domain assumption The composite channel \hat{h}_k is complex Gaussian with mean and variance as in Eqs. 22-23
Section IV-C uses this to derive outage probability, but the mean/variance expressions are inconsistent with the Rician decomposition.
standard math Rician factor κ and Jakes Doppler spectrum describe the HST channel
Standard model, but the specific values are chosen in Table I.

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Cite this review

Pith. "Pith review of Performance Analysis of RIS-Aided High-Mobility Wireless Systems." pith.science (2026). https://pith.science/paper/EWQFP3N6

@misc{pith2026250815375,
  author       = {Pith},
  title        = {Pith review of: Performance Analysis of RIS-Aided High-Mobility Wireless Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EWQFP3N6}},
  note         = {Machine review of arXiv:2508.15375}
}

read the original abstract

Reconfigurable intelligent surface (RIS) technology holds immense potential for increasing the performance of wireless networks. Therefore, RIS is also regarded as one of the solutions to address communication challenges in high-mobility scenarios, such as Doppler shift and fast fading. This paper investigates a high-speed train (HST) multiple-input single-output (MISO) communication system aided by a RIS. We propose a block coordinate descent (BCD) algorithm to jointly optimize the RIS phase shifts and the transmit beamforming vectors to maximize the channel gain. Numerical results are provided to demonstrate that the proposed algorithm significantly enhances the system performance, achieving an average channel gain improvement of 15 dB compared to traditional schemes. Additionally, the introduction of RIS eliminates outage probability and improves key performance metrics such as achievable rate, channel capacity, and bit error rate (BER). These findings highlight the critical role of RIS in enhancing HST communication systems.

Figures

Figures reproduced from arXiv: 2508.15375 by the authors.

**Figure 2.** (a) Channel Gain versus time slots; (b) Achievable ra [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** (a) BER versus AP position; (b) Outrage probability [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

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Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

Future railway services-oriented mobile communication s network,

B. Ai et al. , “Future railway services-oriented mobile communication s network,” IEEE Commun. Mag. , vol. 53, no. 10, pp. 78–85, Oct 2015

work page 2015

[2] [2]

High-speed railway communic ations: From GSM-R to LTE-R,

R. He, B. Ai, G. Wang, K. Guan, Z. Zhong, A. F. Molisch, C. B. Rodriguez, and C. P . Oestges, “High-speed railway communic ations: From GSM-R to LTE-R,” IEEE V eh. Technol. Mag., vol. 11, no. 3, pp. 49–58, Sep 2016

work page 2016

[3] [3]

Performa nce analysis of 5G mobile relay systems for high-speed trains,

J. Zhang, H. Du, P . Zhang, J. Cheng, and L. Y ang, “Performa nce analysis of 5G mobile relay systems for high-speed trains,” IEEE J. Sel. Areas Commun., vol. 38, no. 12, pp. 2760–2772, Dec 2020

work page 2020

[4] [4]

5G for railways: The next generation railway dedicated communications,

R. He et al. , “5G for railways: The next generation railway dedicated communications,” IEEE Commun. Mag. , vol. 60, no. 12, pp. 130–136, Dec 2022

work page 2022

[5] [5]

Challenges toward wireless communications for high-spe ed railway,

B. Ai et al., “Challenges toward wireless communications for high-spe ed railway,” IEEE Trans. Intell. Transp. Syst., vol. 15, no. 5, pp. 2143–2158, Oct 2014

work page 2014

[6] [6]

Smart radio environments emp owered by reconﬁgurable intelligent surfaces: How it works, state of research, and the road ahead,

M. Di Renzo, A. Zappone, M. Debbah, M.-S. Alouini, C. Y uen , J. de Rosny, and S. Tretyakov, “Smart radio environments emp owered by reconﬁgurable intelligent surfaces: How it works, state of research, and the road ahead,” IEEE J. Sel. Areas Commun. , vol. 38, no. 11, pp. 2450–2525, Nov. 2020

2020

[7] [7]

Reconﬁgurable intelligent surfaces: Principles and oppo rtunities,

Y . Liu, X. Liu, X. Mu, T. Hou, J. Xu, M. Di Renzo, and N. Al-Dh ahir, “Reconﬁgurable intelligent surfaces: Principles and oppo rtunities,” IEEE Commun. Surv. Tutorials , vol. 23, no. 3, pp. 1546–1577, third quarter 2021

work page 2021

[8] [8]

Reconﬁgurable-intelligent-s urface- assisted B5G/6G wireless communications: Challenges, sol ution, and future opportunities,

Z. Chen, G. Chen, J. Tang, S. Zhang, D. K. So, O. A. Dobre, K. K. Wong, and J. Chambers, “Reconﬁgurable-intelligent-s urface- assisted B5G/6G wireless communications: Challenges, sol ution, and future opportunities,” IEEE Commun. Mag. , vol. 61, no. 1, pp. 16–22, Jan. 2023

work page 2023