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REVIEW 4 major objections 6 minor 11 references

RIS Assisted Wireless Communication: Advanced Modeling, Simulation, and Analytical Insights

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read RIS-assisted links gain an aperture-induced ISI: larger arrays and faster symbols raise BER, and matching alignment recovers part of it.

desk verdict A plausible qualitative study of aperture-fill-time ISI in RIS links, undercut by a genie-aided alignment comparison and sloppy passband/baseband math. read the letter →

arxiv 2501.15917 v1 pith:LIYXZOVE submitted 2025-01-27 physics.app-ph

Xiaocun Zong , Fan Yang , Zhijun Zhang , Shenheng Xu , Maokun Li This is my paper · ORCID
classification physics.app-ph
keywords Reconfigurableintelligentsurfaceaperturefilltimebiterrorrateinter-symbolinterferenceQPSKwaveform-levelsimulationarraysizeoptimalmatchingalignment
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 reconfigurable intelligent surfaces add a form of self-inflicted multipath: because the elements sit at different distances from the feed and the receiver, a single wideband symbol arrives as many delayed copies, and the timing spread, the aperture fill time, grows with array size and with faster symbol rates. The authors build an end-to-end waveform simulation that starts from QPSK bits and follows the signal through 1-bit phase adjustment, per-element delay, superposition, alignment, demodulation, and decision, and they show BER rises sharply once the aperture delay spread approaches a symbol period. They also propose an alignment step that uses the cross-correlation peak between the merged received signal and the original transmitted waveform, and they report that this optimal matching alignment lowers BER compared with pilot-based alignment. If this is right, RIS link design must treat the surface itself as a delay-spreading channel, not as an ideal phase-shifting mirror.

What carries the argument

The load-bearing object is the aperture fill time, the time spread across the RIS aperture between the earliest and latest element paths, expressed in sampling intervals via $r_f^{mn} + r_{\mathrm{eye}}^{mn}$ divided by $c$ and $dt$. The simulation pipeline turns each RIS element into a delayed copy of the same QPSK waveform, applies a 1-bit phase quantization so a $\pi$ phase state becomes a half-carrier-period circular shift, superposes all element signals, and then estimates a global delay by maximizing the cross-correlation (the intertwined function) between the merged signal and the original transmitted waveform. This machinery converts an antenna-array property into a communication-channel impairment, letting the paper sweep array size, symbol frequency, and emission angle and read off BER as the output variable.

What would settle it

Take the same $100 \times 1$ ULA simulation and run Algorithm 3 with only a short pilot segment or a decision-directed estimate of the transmitted waveform as the reference, rather than the true $S(t)$; if the BER near three symbol periods of edge delay stays near the reported value, the claim survives, and if it reverts to pilot-alignment levels, the gain is an artifact of knowing the transmitted signal.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a RIS creates a programmable, aperture-induced multipath from its own array: each element's feed-to-element and element-to-receiver distances impose a distinct delay on the same QPSK waveform, 1-bit phase quantization forces some paths to shift by half a carrier period, and the superposition of these delayed copies produces inter-symbol interference at code transitions. The quantitative evidence is a set of waveform simulations: a $100 \times 1$ ULA at $f_{\mathrm{symbol}}/f_c = 1/2$ gives 25.5% BER under normal incidence, the same array at $f_{\mathrm{symbol}}/f_c = 1/20$ gives error-free reception, and steering a $10 \times 1$ array to 50 degrees gives 21% BER. The authors then show that aligning the merged signal by the peak of its cross-correlation with the transmitted waveform, the intertwined function, shifts the BER-versus-delay curve down substantially relative to pilot alignment, indicating that most of the aperture-fill penalty is recoverable when the reference signal is known.

Load-bearing premise

Algorithm 3's BER reduction is computed with the transmitted QPSK waveform as the cross-correlation reference, so the receiver is assumed to know the exact transmitted signal rather than only a pilot.

Editorial extensions

If this is right

  • At fixed carrier frequency, increasing RIS array size raises the maximum element delay, so the aperture fill effect grows and BER rises even at high SNR.
  • Raising the baseband symbol frequency shortens each symbol period, so the same physical delay spans more symbols; the paper shows $f_{\mathrm{symbol}}/f_c = 1/2$ with a $100 \times 1$ array gives 25.5% BER while $1/20$ gives error-free reception.
  • Steering the output beam to large angles makes edge-element delays asymmetric and multiplies the delay spread; the paper reports 21% BER for a $10 \times 1$ array at 50 degrees and 44.5% for a $100 \times 1$ array at that angle.
  • Optimal matching alignment, which cross-correlates the merged received signal against the transmitted waveform, reduces BER for a given average channel delay compared with pilot-based alignment.
  • A RIS therefore introduces multipath-like ISI from its own aperture, a distinction from a single-antenna system where multipath comes only from the environment.

Reading between the lines

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

  • Editorial extension: because Algorithm 3 requires the true transmitted waveform, the reported gain is best read as an upper bound for data-aided processing; a real receiver would need a known preamble or decision-directed estimate to approach it.
  • Editorial extension: the same aperture delay-spread mechanism should appear in OFDM as carrier-dependent phase rotation and inter-carrier interference, so the QPSK-specific BER result is likely a special case of a broader wideband RIS impairment.
  • Editorial extension: one testable design rule follows directly from the simulated scaling: for a fixed target BER, the tolerable array aperture shrinks as symbol rate rises and as the output beam steers away from broadside; sweeping these two parameters in one experiment would confirm the claimed equivalence.
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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 / 6 minor

Summary. The manuscript proposes a joint simulation framework for RIS-assisted wireless communication that combines array-level antenna modeling with end-to-end communication signal processing. Using QPSK as an example, the authors simulate signal generation, modulation, propagation, RIS phase adjustment, multi-channel delay, reception, alignment, demodulation, and decision, and they report BER results as functions of SNR, array size, baseband symbol frequency, and emission angle. The main claims are that larger arrays and higher baseband frequencies increase aperture-fill-time-induced inter-symbol interference and BER, and that a proposed 'optimal matching' alignment algorithm based on cross-correlation with the original transmitted waveform substantially reduces BER relative to a pilot-based alignment baseline.

Significance. If the claims were valid, the paper would offer a useful simulation methodology for RIS links that captures physical element delays rather than idealizing the RIS as a point scatterer, and it would quantify an aperture fill time effect that is often neglected in communication-level RIS models. The paper also has the merit of attempting an end-to-end validation against a known AWGN QPSK result. However, the central positive claim about the alignment algorithm is undermined by an unfair comparison that gives the proposed method access to the full transmitted waveform, and the analytical formulation contains dimensional inconsistencies. The work is therefore a potentially interesting simulation study, but its headline conclusions are not currently supported by the evidence presented.

major comments (4)
  1. [Section VI, Algorithm 3 and Fig. 17] The optimal matching alignment algorithm computes the cross-correlation F(tau) between the merged received signal R(t) and S(t), where Algorithm 3 explicitly lists the 'Original QPSK Signal: S(t)' as an input and the baseline pilot method uses only the first two bits as a pilot. In a real link the receiver does not know the full transmitted data waveform, so the proposed method is given genie-aided information that the pilot-based baseline lacks. The BER reduction shown in Fig. 17 is therefore a consequence of this information asymmetry rather than a demonstrated algorithmic advantage, and the abstract's and conclusions' claim that the proposed algorithm 'significantly reduces BER' is unsupported.
  2. [Section II, Eqs. (1) and (2)] Equation (1) begins with a delayed signal s(t - tau_mn) and then replaces s(t) by exp(j 2 pi f_c t), which mixes a real passband representation with a complex exponential notation; for a QPSK signal with I/Q modulation, s(t) should be a real passband waveform, and the reduction to h * s(t) is not justified without a clear complex-baseband conversion. Equation (2) further adds a phase phi^Q_mn (which is 0 or pi in Eq. (4)) to a time delay tau^Path_mn, which is dimensionally inconsistent; this needs to be reformulated with a common unit or with an explicit frequency conversion before the analytical framework can be assessed.
  3. [Section V and Figs. 14-15] All reported BER values are obtained from single random bitstreams of length 200 bits. For example, the 25.5% and 21% BER values are single-sample estimates with a standard deviation of roughly 3-4 percentage points, and the claim of 'no BER' for the small-delay cases cannot be distinguished from the finite-sample floor. The paper therefore does not provide statistically reliable evidence for the quantitative BER trends, and this weakens the central aperture-fill-time claim.
  4. [Section III] The verification against the theoretical QPSK AWGN BER is performed with a 1x1 array, which by construction eliminates all element-dependent delays and phase adjustments that are the subject of the RIS model. The agreement in Fig. 10 thus validates only the noise generation, demodulation, and decision chain; it does not validate the RIS delay and phase processing steps that distinguish this framework from a conventional baseband AWGN simulator.
minor comments (6)
  1. [Introduction] There is a spelling error: 'dicision' should be 'decision' in the phrase 'alignment, dicision, and post-processing'.
  2. [Fig. 13 caption] The caption contains a typo: 'Uniform Linear Aarry' should be 'Uniform Linear Array'.
  3. [Algorithm 3] The declared output of Algorithm 3 is 'Original QPSK Signal: S(t)', but the algorithm actually returns the aligned received signal R'(t); the output specification should be corrected.
  4. [Section VI] The term 'intertwined function' is nonstandard; the operation defined in Algorithm 3 is a cross-correlation, and using standard terminology would improve clarity.
  5. [Section II, Eq. (5)] The sampling time dt in Eq. (5) is not defined in the text, and the relationship between the number of delayed sample points and the physical propagation delay should be stated explicitly.
  6. [General] No code or pseudocode details are provided for the channel propagation and signal merging steps, and several MATLAB-like functions such as 'circshift' and 'finddelay' are used without formal definitions; since reproducibility is claimed, a code release or a complete algorithmic description would be needed.

Circularity Check

1 steps flagged · score 7.0 of 10

Algorithm 3's optimal matching uses the complete transmitted QPSK waveform S(t) as its correlation reference, so the claimed BER reduction over pilot-based alignment is predetermined by awarding the proposed method information unavailable to a real receiver.

  1. self definitional [Section VI, Algorithm 3, lines 1-4; compared with the pilot-based baseline in Algorithm 1 and Fig. 17]
    "Algorithm 3 Optimal Matching Alignment Algorithm Input: Merged received signal: R(t) Output: Original QPSK Signal: S(t) 1: Calculate the intertwined function F (τ ) of R(t) and S(t): F (τ ) =R(t) ∗ S(t) = R +∞ −∞ R∗(t)S(t + τ )dt 2: Find the maximum value of the function F (τ ) and mark its coordinates as Tdelay = τ (max(F (τ )) 3: Shift the signal R(t) by a distance τ: Tdelay = f inddelay(R(t), S(t)); R′(t) = circshif t(R(t), Tdelay); 4: return R’(t)"

    The alignment algorithm that produces the paper's headline BER reduction is defined as the shift maximizing cross-correlation with S(t), the complete originally transmitted QPSK waveform containing all random data bits. In the manuscript's own setup, the receiver's only known reference is the two-bit pilot fixed by Algorithm 1 ('the first two bits are specified as [0,1] and used as pilot sequence'), and the baseline 'pilot-based alignment' is restricted to that pilot. Algorithm 3 therefore has access to the very transmitted data being recovered, while the baseline does not; the BER advantage shown in Fig. 17 is largely an artifact of the reference-sequence length difference, not a demonstrated alignment principle.

full rationale

The paper's other main result — that larger array sizes and higher baseband symbol frequencies increase aperture-fill-time-induced BER — is a self-contained simulation observation: the model computes per-element delays from geometry, sums the delayed waveforms, and measures BER; no fitted parameter is renamed as a prediction, and no load-bearing self-citation supports it. Section III's AWGN validation against the textbook QPSK expression also provides an independent check. The circularity is concentrated in the alignment-optimization claim. Algorithm 3 defines optimal matching as cross-correlation with the full transmitted QPSK signal S(t); baseline alignment uses only a two-bit pilot (Algorithm 1). Giving the proposed method the complete data-bearing waveform as a reference while withholding it from the baseline forces the BER improvement in Fig. 17; the algorithm is effectively handed the correct answer (the transmitted bits) and then evaluated against that same answer. This is a constructed-input comparison rather than an exact equation identity, so the score is severe but not maximal. Self-citations in the references are incidental and not load-bearing. Overall, the central new algorithmic contribution is substantially circular, while the aperture-fill-time observations remain non-circular, yielding a score of 7.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

No new physical entities are introduced. The central results rest on a narrowband delayed-element channel model, a data-aided alignment assumption, and small-sample BER estimates; none of these are externally validated with code, measurements, or full-wave simulation.

free parameters (3)
  • Element attenuation coefficient eta_mn = 1
    Equation (1) sets per-element channel attenuation to 1 by default, idealizing away element loss and gain; absolute BER versus SNR curves depend on this normalization.
  • Baseband-to-carrier frequency ratio fsymbol/fc = 1/2 and 1/20
    Section V selects these ratios to illustrate aperture fill; they are hand-picked simulation conditions, not derived from a theory.
  • BER bitstream length = 200
    Section V computes BER from single random bit streams of length 200, with no repetition or statistical justification.
assumptions (5)
  • domain assumption Narrowband scalar channel model: r(t) = sum eta_mn s(t - tau_mn) with s(t) = exp(j 2 pi fc t).
    Equation (1) treats each RIS element as a complex gain plus delay, ignoring mutual coupling, element radiation patterns, polarization, and wideband dispersion.
  • ad hoc to paper A 1-bit phase shift of pi is equivalent to delaying the real passband waveform by one half carrier period.
    Algorithm 2 uses circshift by 1/(2 fc dt); this equates phase and time delay and assumes the baseband envelope is unaffected.
  • ad hoc to paper The receiver has access to the original transmitted signal S(t) for alignment.
    Algorithm 3 takes S(t) as input; this is unavailable in a real communication system and is not assumed for the pilot baseline.
  • ad hoc to paper A 200-symbol random bitstream is representative for BER estimation.
    Section V reports 21%, 25.5%, and 44.5% BER from one 200-bit stream; a single bit error changes the estimate by 0.5 percentage points.
  • standard math QPSK AWGN bit error probability Pb = 0.5 erfc(sqrt(Eb/N0)).
    Equation (6), a standard textbook reference used for simulation verification.

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Cite this review

Pith. "Pith review of RIS Assisted Wireless Communication: Advanced Modeling, Simulation, and Analytical Insights." pith.science (2026). https://pith.science/paper/LIYXZOVE

@misc{pith2026250115917,
  author       = {Pith},
  title        = {Pith review of: RIS Assisted Wireless Communication: Advanced Modeling, Simulation, and Analytical Insights},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LIYXZOVE}},
  note         = {Machine review of arXiv:2501.15917}
}
read the original abstract

This article presents a novel perspective to model and simulate reconfigurable intelligent surface (RIS)-assisted communication systems. Traditional methods in antenna design often rely on array method to simulate, whereas communication system modeling tends to idealize antenna behavior. Neither approach sufficiently captures the detailed characteristics of RIS-assisted communication. To address this limitation, we propose a comprehensive simulation framework that jointly models RIS antenna design and the communication process. This framework simulates the entire communication pipeline, encompassing signal generation, modulation, propagation, RIS-based radiation, signal reception, alignment, demodulation, decision, and processing. Using a QPSK-modulated signal for validation, we analyze system performance and investigate the relationship between bit error rate (BER), aperture fill time, array size, and baseband symbol frequency. The results indicate that larger array sizes and higher baseband symbol frequencies exacerbate aperture fill time effects, leading to increased BER. Furthermore, we examine BER variation with respect to signal-to-noise ratio (SNR) and propose an optimal matching-based alignment algorithm, which significantly reduces BER compared to conventional pilot-based alignment methods. This work demonstrates the entire process of RIS communication, and reveals the source of bit errors, which provides valuable insights into the design and performance optimization of RIS-assisted communication systems.

Figures

Figures reproduced from arXiv: 2501.15917 by the authors.

Figure 5
Figure 5. Element Phase Adjustment. Elements at different locations will produce different phase adjustments to the signal. Since it is a 1-bit adjustment, two [PITH_FULL_IMAGE:figures/full_fig_p002_5.png] view at source ↗
Figure 1
Figure 1. Concept diagram of RIS-assisted wireless communication. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Simplified diagram of channel model [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figures from the paper (11 more)
Figure 6
Figure 6. Figure 6: The signal after RIS adjustment is 180◦ out of phase. C. Multi-Channel Signal Delay and Merge Since the position of each element is different relative to the transmit horn and receive horn, the signal will have different degrees of delay when it incidents the elements …
Figure 8
Figure 8. Figure 8: Merge and the signal disturbance at the alternation of codes. [PITH_FULL_IMAGE:figures/full_fig_p004_8.png]
Figure 9
Figure 9. Figure 9: Signal post-processing: (a) Alignment of received signal with original [PITH_FULL_IMAGE:figures/full_fig_p004_9.png]
Figure 10
Figure 10. Figure 10: Comparison chart of algorithm correctness verification. [PITH_FULL_IMAGE:figures/full_fig_p005_10.png]
Figure 11
Figure 11. Figure 11: 16 × 16 RIS simulation results:(a) constellation diagram; (b) bit error rate curve. Essentially, array size and baseband frequency have the same effect on aperture fill effect, both affecting the relative size of symbol delay. The relationship between the two is shown…
Figure 12
Figure 12. Figure 12: Factors affecting aperture fill time [PITH_FULL_IMAGE:figures/full_fig_p006_12.png]
Figure 13
Figure 13. Figure 13: Concept of Uniform Linear Aarry. significantly reduced at this time. In fact, from Fig.14(c) we can see, this situation is completely equivalent to changing the array to 10×1 while the frequency remains fsymbol/fc = 1/2. After the signal is demodulated using pilot ali…
Figure 16
Figure 16. Figure 16: BER under different ULA conditions. VI. ALIGNMENT ALGORITHM OPTIMIZATION BASED ON OPTIMAL MATCHING Currently, the mainstream alignment method is based on pilot alignment. For the RIS multipath signal, the pilot of the first arriving signal is used as the reference for…
Figure 14
Figure 14. Figure 14: Simulation results for different array sizes and baseband bandwidths at 0°:(a)array size [PITH_FULL_IMAGE:figures/full_fig_p007_14.png]
Figure 15
Figure 15. Figure 15: Simulation results for different array sizes and baseband bandwidths at [PITH_FULL_IMAGE:figures/full_fig_p007_15.png]
Figure 17
Figure 17. Figure 17: BER versus the average number of shifted symbols. [PITH_FULL_IMAGE:figures/full_fig_p008_17.png]

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Works this paper leans on

11 extracted references · 8 canonical work pages

  1. [1]

    A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,

    W. Saad, M. Bennis, and M. Chen, “A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,” IEEE Netw., vol. 34, no. 3, pp. 134–142, May/Jun. 2020

    2020

  2. [2]

    6G wireless networks: Vision, requirements, architec- ture, and key technologies,

    Z. Zhang et al., “6G wireless networks: Vision, requirements, architec- ture, and key technologies,” IEEE Veh. Technol. Mag. , vol. 14, no. 3, pp. 28–41, Sep. 2019

    2019

  3. [3]

    Five disruptive technology directions for 5G,

    F. Boccardi, R. W. Heath, Jr., A. Lozano, T. L. Marzetta, and P. Popovski, “Five disruptive technology directions for 5G,” IEEE Commun. Mag. , vol. 52, no. 2, pp. 74–80, Feb. 2014

    work page 2014

  4. [4]

    Wireless communications through reconfigurable intelligent surfaces,

    E. Basar, M. Di Renzo, J. De Rosny, M. Debbah, M. Alouini, and R. Zhang, “Wireless communications through reconfigurable intelligent surfaces,” IEEE Access, vol. 7, pp. 116753–116773, 2019

    work page 2019

  5. [5]

    Smart radio environments empowered by recon- figurable intelligent surfaces: How it works, state of research, and road ahead,

    M. D. Renzo et al. , “Smart radio environments empowered by recon- figurable intelligent surfaces: How it works, state of research, and road ahead,” IEEE J. Sel. Areas Commun. , vol. 38, no. 11, pp. 2450–2525, Nov. 2020

    work page 2020

  6. [6]

    Reconfigurable intelligent surfaces: Principles and oppor- tunities,

    Y . Liu et al., “Reconfigurable intelligent surfaces: Principles and oppor- tunities,” IEEE Commun. Surveys Tuts. , vol. 23, no. 3, pp. 1546–1577, 3rd Quart., 2021

    work page 2021

  7. [7]

    Towards smart and reconfigurable environment: Intelligent reflecting surface aided wireless network,

    Q. Wu and R. Zhang, “Towards smart and reconfigurable environment: Intelligent reflecting surface aided wireless network,” IEEE Commun. Mag., vol. 58, no. 1, pp. 106–112, Jan. 2020

    2020

  8. [8]

    Holographic MIMO surfaces for 6G wireless networks: Opportunities, challenges, and trends,

    C. Huang et al., “Holographic MIMO surfaces for 6G wireless networks: Opportunities, challenges, and trends,” IEEE Wireless Commun., vol. 27, no. 5, pp. 118–125, Oct. 2020

    work page 2020

Show all 11 references
  1. [9]

    Huang and J

    J. Huang and J. A. Encinar, Reflectarray Antennas , 1st ed. Hoboken, NJ, USA: Wiley, 2008

    2008

  2. [10]

    Reconfigurable reflectarrays and array lenses for dynamic antenna beam control: A review,

    S. V . Hum and J. Perruisseau-Carrier, “Reconfigurable reflectarrays and array lenses for dynamic antenna beam control: A review,” IEEE Trans. Antennas Propag., vol. 62, no. 1, pp. 183–198, Jan. 2014

    2014

  3. [11]

    Beam scanning reflectarray antennas: A technical overview and state of the art,

    P. Nayeri, F. Yang, and A. Z. Elsherbeni, “Beam scanning reflectarray antennas: A technical overview and state of the art,” IEEE Antennas Propag. Mag., vol. 57, no. 4, pp. 32–47, Aug. 2015

    2015

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