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The ISAC systems aided by MIMO, RIS and with Beamforming Techniques

T0 review · 5 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A single base station with two static RISs and a user-supplied height can localize a user within 1.5 meters in over 98 percent of simulated trials, using time-multiplexed sensing and communication.

desk verdict A concrete but modest single-BS ISAC architecture; the 98% localization claim rests on an untested perfect-path-identification assumption, so treat the headline number as optimistic. read the letter →

arxiv 2505.03090 v1 pith:YQ3YCUGH submitted 2025-05-06 eess.SY cs.SYstat.AP

Rafael Augusto Pedriali , Isadora Martines Ferreira , Jose Carlos Marinello , Taufik Abrao This is my paper · ORCID
classification eess.SYcs.SYstat.AP
keywords sensingsystemcommunicationarrivalbeamformingneedsystemstechniques
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

Wireless networks increasingly want to both communicate with devices and sense their location using the same radio signal. This paper describes a system with one base station and two flat reflective surfaces called RISs. The base station sends a short sensing pulse; the pulse bounces off the user and returns directly to the base station and also via the two reflective surfaces. By measuring the round-trip travel times, the system can draw three imaginary spheres around the base station and the two surfaces. The user lies where the spheres intersect. This normally gives two possible locations, so the system asks the user to send its height in a short reply frame, which removes the ambiguity. The reflective surfaces are configured once and then kept fixed, avoiding the cost of adjusting them constantly. The base station uses three antennas for the wide sensing beam and 31 antennas for a narrow communication beam. The paper simulates this design in a Rice fading channel and reports that a user is located within 1.5 meters in more than 98 percent of 5,000 Monte Carlo trials, and that the sensing energy per cycle is about 0.4 millijoules. The claimed benefits are lower complexity and lower power than earlier designs that needed several base stations or dynamic reconfiguration.
Extended reading notes

Core claim

The proposed ISAC system, with one BS, two static RISs, time multiplexing, and user height feedback, achieves localization success exceeding 98% within a 1.5 m threshold (Section V.C), O(1) sensing complexity (Section III.F), and sensing energy of 0.4 mJ per cycle (Section V.A), while removing three of four sensing anchors used in traditional trilateration.

Load-bearing premise

The system assumes the UE always sends its height in the Permission and Response Frame when it detects the Sensing Frame; the paper states this "is critical for this ISAC model to function effectively" (Section III.E). If the user does not cooperate, is out of coverage, or reports an incorrect height, the BS cannot choose between the two candidate positions produced by three-anchor trilateration, and localization fails. This makes the sensing partially cooperative rather than a passive monostatic radar capability.

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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

5 major / 5 minor

Summary. The paper proposes an ISAC system in which a single base station (BS) and two static RISs perform three-dimensional user localization by time-multiplexing communication and sensing frames. A sensing signal is reflected by the user along three modeled paths: a direct path back to the BS, a path via RIS 1, and a path via RIS 2. The BS estimates ToA and uses a simplified AoA correlation procedure to identify the three paths, then solves a linearized trilateration system with Cramer's rule; the two-candidate ambiguity in the height coordinate is resolved using user-reported height in a Permission and Response Frame. The RISs are configured once using passive beamforming, and the paper reports O(1) sensing complexity, 0.4 mJ energy per sensing cycle, and a Monte Carlo localization success rate above 98% within a 1.5 m error threshold. A probabilistic ISAC error model combining localization success and BER is also presented and compared with joint simulation.

Significance. If the central claims hold, the system offers an interesting single-BS, two-RIS ISAC architecture with very low sensing energy and constant localization complexity, which would be a useful data point for low-cost ISAC deployment. The explicit time-frame design, the analytic linearization of the trilateration problem, the complexity comparison against MUSIC- and STCM-based systems, and the 5000-trial Monte Carlo evaluation are concrete strengths. The significance is weakened, however, by three issues: the 98% success claim is conditional on an unverified exactly-three-path assumption with no modeled path-misclassification; the 'probabilistic' validation model shares its localization component with the simulation, so the agreement in Fig. 7.c does not independently validate the localization error model; and the sensing operation is cooperative rather than passive because it relies on the user reporting height. These limitations are addressable, but they currently bound the strength of the paper's conclusions.

major comments (5)
  1. [§V.C, last paragraph; §III.D] The claim that 'two or more random reflections in cascade eliminate any possibility of creating alternative paths beyond those identified as Path1, Path2, and Path3' is unsupported. The paper provides no quantitative path-loss or delay-spread analysis for higher-order reflections, and the Monte Carlo error model in Section IV.B injects only ToA noise while assuming that the AoA-based path identification in Eqs. (35)-(36) is always correct. If a fourth path arrives with non-negligible power, or if the direct path is mislabeled as a RIS path, the max-correlation rules can feed the wrong distance into Eqs. (29)-(31) and produce an incorrect trilateration solution. The >98% success rate is therefore conditional on perfect path identification. This assumption should be tested explicitly, e.g., by simulating cascaded reflections with a multipath model and by adding a path-misclassification probability to the error model.
  2. [§III.D, Eq. (34)] Equation (34) contains an apparent typo: the round-trip time for Path 3 is written as t3 = (d1 + d2 + dR2-BS)/c + ε3, but it should use d3, the distance between the user and RIS 2, in place of d2. As written, the formula describes the same user-RIS distance as Path 2 and would produce a systematically wrong distance estimate for the third anchor. The authors should correct Eq. (34) and confirm that the reported Monte Carlo results, especially the 98% success rate in Section V.C, are reproduced with the corrected formula.
  3. [§IV.B, §IV.C, Fig. 7.c] The validation of the probabilistic ISAC error model is partially circular. The 'probabilistic' curve in Fig. 7.c is obtained from the complement of Eq. (38), but the localization term P({unsuccessful location}) is taken from the simulated success rate SRSENS_sim in Eq. (42), not from an independent analytical model. Consequently, the agreement between the probabilistic and simulated curves in Fig. 7.c validates only the substitution of the theoretical BER for the simulated BER, not the localization error model itself. To support the stated validation claim, the authors should derive an analytic or semi-analytic characterization of the localization error distribution that does not reuse the same Monte Carlo samples, or alternatively re-label the comparison as a consistency check.
  4. [§III.E, §III.A, Fig. 2] The localization mechanism requires the user to transmit its height inside the Permission and Response Frame whenever it detects the Sensing Frame; the paper itself states that this information 'is critical for this ISAC model to function effectively.' This makes the sensing operation cooperative and limits the system to users that respond correctly. The failure mode for a non-cooperative user, an out-of-coverage user, or a user reporting an incorrect height is not quantified, and the paper's framing of the system as replacing three additional sensing BSs should be qualified accordingly. This limitation should be stated explicitly and, if possible, analyzed with a sensitivity study on height-report error.
  5. [§V.A, Table II] The RIS configuration procedure is post-hoc. The paper reports that the first attempt, programming the RIS to cover the first quadrant (1Q), was inadequate, and that the RIS elements were reprogrammed based on the BS-to-RIS path to cover the second quadrant (2Q) before the system worked. This means the static RIS configuration is not derived from a systematic optimization or a stated design rule; the reported coverage heat map in Fig. 6 and the subsequent 98% localization success are tied to a configuration found by trial and error. The authors should either provide a deterministic rule for choosing the RIS phase configuration for a given environment or include a sensitivity analysis over RIS configurations to show that the result is robust rather than configuration-specific.
minor comments (5)
  1. [Throughout] The manuscript contains numerous typographical and grammatical errors, including the section heading 'Beanforming passivo' and the phrase 'with a W GN channel'; a thorough language edit is needed before publication.
  2. [§III.C, Eqs. (22)-(25)] The notation for measured versus actual distances is inconsistent: Eq. (22) minimizes over di, described as 'actual distances,' while Eq. (24) and the surrounding text refer to measured distances d. Using distinct symbols for measured and true distances would clarify the optimization problem.
  3. [§V.C, Fig. 7] The Monte Carlo curves are reported without error bars or confidence intervals. Given that the central claim is a success rate above 98% from 5000 trials, the authors should report the statistical uncertainty of that estimate.
  4. [§IV.A, Eq. (40)] The BER expressions in Eq. (40) are written for the normalized channel h, but the SNR variable in the figures is labeled |h|^2 Eb/N0; the connection between these quantities should be stated explicitly so that the reader can relate the BER curves to the localization SNR thresholds.
  5. [§V.A, Table II] The 'Passive Beamforming Target (RIS)' parameter is listed as (-55, 5, 0), which lies outside the monitored region shown in Fig. 4 and Fig. 6; the meaning of this parameter and its role in the configuration should be explained.

Circularity Check

2 steps flagged · score 6.0 of 10

Two validation moves are circular: the probabilistic ISAC model is an algebraic rearrangement of the simulated localization success rate, and the path-uniqueness conclusion restates the three-path model.

  1. self definitional [Section IV.C and Section V.C, Figure 7.c]
    "Although these models differ, both utilize the location error threshold. The key distinction lies in the formulation of the communication analysis integrated with ISAC. The probabilistic model relies on the theoretical bit error probability for BPSK Eq, (40), with the sensing analysis derived from Eq. (42), whereas the simulation results are directly implemented using Eq. (44). Despite being derived through different methodologies, both models produce identical results, validating the integration analysis between the localization and communication systems."

    Eq. (42) defines SRSENS_sim, the simulated localization success rate used in the Monte Carlo. Eq. (44) defines SRISAC_sim by the same localization condition ∥p_i−p̂_i∥≤ε_Lth conjoined with correct bit detection. The 'probabilistic analytical model' is built from Eq. (38) with P(unsuccessful location)=1−SRSENS_sim (Eq. (43)) and P(commun. error)=BER, so the ISAC success it outputs is algebraically SRSENS_sim×(1−BER), the expected value of Eq. (44). The two 'different methodologies' therefore share the identical localization event and noise/threshold; the agreement is by construction, not an independent validation.

  2. self definitional [Section V.C, final paragraph]
    "Lastly, simulation tests were conducted and revealed that two or more random reflections in cascade eliminate any possibility of creating alternative paths beyond those identified as Path1, Path2, and Path3. This behavior is also observed in the project [15] validating our multiple random reflections."

    Section III.A defines the sensing signal as returning 'through three distinct paths', and the Monte Carlo in Section IV.B records ToA only for those anchors, identifying them via AoA without any path-misclassification or fourth-path mechanism. The V.C claim that random cascade reflections 'eliminate any possibility' of alternative paths is thus a restatement of the model's input: a simulation built on exactly three paths cannot reveal the absence of a fourth. This assumption is load-bearing for the >98% localization success because Eqs. (35)-(36) are assumed to label paths correctly, and no misclassification probability enters Eq. (42).

full rationale

The paper's own localization simulation is not inherently circular: it evaluates Eq. (42) under a stated ToA error model and threshold, and that result is an honest conditional simulation. However, two claimed validations reduce to construction. First, the probabilistic ISAC model in Eq. (38) uses Eq. (42)'s simulated localization success rate and the theoretical BER as inputs, so its agreement with the joint simulation Eq. (44) is an identity rather than a prediction. Second, the assertion that cascade reflections eliminate all paths beyond Path1-3 is built into the three-path model of Section III.A, and the Monte Carlo generates only those anchors, so the robustness conclusion is assumed, not discovered. These moves are load-bearing for the 98% claim only insofar as the paper relies on perfect path identification; the 98% figure itself is a conditional simulation result. No self-citation chain carries the derivation; the one self-authored citation [17] is used only as a comparison baseline and is not load-bearing.

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

No new physical entities are introduced; the system combines known hardware, BS, RIS, MIMO, with a protocol for user height feedback. The main additional burden comes from hand-tuned RIS phase settings, the assumed ToA error model, and the cooperative user assumption.

free parameters (3)
  • Static RIS phase configuration = Adjusted from first-quadrant to second-quadrant aiming point after failed initial test
    Section V.A reports the initial BS-to-RIS-1Q configuration was inadequate and the RIS was reprogrammed to cover 2Q. This is a hand-tuned environmental calibration that affects coverage and sensing gain.
  • Localization success threshold epsilon_Lth = 1.5 m
    Chosen to match the 3 m beamwidth of the 31-antenna communication beam; it is a design criterion rather than a derived quantity.
  • ToA error variance scaling = N(0, |h|^2/SNR) with SNR=20 dB in the middle-case scenario
    Section V.C calibrates the noise variance using a middle-case SNR from the visibility heat map. This choice shapes the reported success rates and is not derived from a physical estimator.
assumptions (5)
  • domain assumption ToA estimation errors are independent Gaussian random variables with variance |h|^2/SNR
    Introduced in Section III.D and Table II; this noise model drives all localization results and is not justified by a physical ranging estimator.
  • domain assumption The user cooperates by transmitting height in the Permission and Response Frame
    Section III.E says this information is critical; the system cannot resolve the two-candidate trilateration ambiguity without it.
  • domain assumption Localization failure and communication error are independent events
    Eq. (38) in Section IV uses the union formula requiring independence; time multiplexing is cited as justification, but the independence is asserted, not proven.
  • ad hoc to paper Beyond the three modeled paths, cascades of two or more reflections produce no usable alternative paths
    Section V.C states simulation tests revealed this; no analytical derivation or physical argument is provided.
  • domain assumption The BS has channel state information sufficient for passive beamforming configuration via Eq. (14)
    Section II.C.1 assumes detailed channel knowledge to set RIS phases; the method to acquire this is not specified.

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Cite this review

Pith. "Pith review of The ISAC systems aided by MIMO, RIS and with Beamforming Techniques." pith.science (2026). https://pith.science/paper/YQ3YCUGH

@misc{pith2026250503090,
  author       = {Pith},
  title        = {Pith review of: The ISAC systems aided by MIMO, RIS and with Beamforming Techniques},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YQ3YCUGH}},
  note         = {Machine review of arXiv:2505.03090}
}
read the original abstract

This paper explores the integration of communication and sensing in modern wireless systems through the configuration of BS and RIS antenna elements. By leveraging time multiplexing for both communication and sensing, the proposed system optimizes spectral efficiency and operational performance. The use of static RIS configurations tailored to specific environments eliminates the need for dynamic reconfigurations, enhancing system agility, reducing processing complexity, and improving sensing accuracy. The system incorporates trilateration, angle of arrival, and time of arrival techniques to enable precise user localization by combining signals reflected along multiple paths. This method helps choose the best connections and lowers sensing costs while preventing interference with communication data, highlighting the need to bring together new technologies like passive and adaptive beamforming in one system.

Figures

Figures reproduced from arXiv: 2505.03090 by the authors.

Figure 1
Figure 1. ISAC system model composed by one BS and 2 RIS. [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗
Figure 2
Figure 2. Proposed ISAC Frame Structure with Time Allocation. [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Path identification based on AoA technique. [PITH_FULL_IMAGE:figures/full_fig_p018_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: ISAC Sensing, a) BS emission; b) UE reflection; c) RIS-Assisted redirection. [PITH_FULL_IMAGE:figures/full_fig_p025_4.png]
Figure 5
Figure 5. Figure 5: Illustration of the ISAC system operating in downlink mode with the BS using 31 antennas. [PITH_FULL_IMAGE:figures/full_fig_p026_5.png]
Figure 6
Figure 6. Figure 6: Visibility region provided by the RIS in relation to the BS. [PITH_FULL_IMAGE:figures/full_fig_p027_6.png]
Figure 7
Figure 7. Figure 7: a) Impact of SNR on success localization rate precision; b) BER performance as a function of SNR for [PITH_FULL_IMAGE:figures/full_fig_p029_7.png]

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