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REVIEW 3 major objections 5 minor 164 references

Interference Management for Integrated Sensing and Communications: A Multiple Access Perspective

T0 review · 3 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read Rate-splitting multiple access is the key to managing interference in integrated sensing and communication (ISAC), delivering the best communication–sensing trade-offs while serving as a framework that contains SDMA and NOMA.

desk verdict A useful tutorial on MA-ISAC whose comparative headline ('RSMA consistently best') rests on a simulation protocol that handicaps NOMA and omits radar-sequence-enabled designs; the expository body stands on its own. read the letter →

arxiv 2509.02352 v1 pith:27J4U5AJ submitted 2025-09-02 eess.SP

Kexin Chen , Yijie Mao , Wonjae Shin , Bruno Clerckx , Christos Masouros This is my paper · ORCID
classification eess.SP
keywords integratedsensingandcommunication(ISAC)multipleaccess(MA)rate-splitting(RSMA)non-orthogonal(NOMA)interferencemanagementdual-functionalwaveformdesign6GnetworksCramér–Raobound
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 tutorial organizes integrated sensing and communication (ISAC) around multiple access (MA) techniques, arguing that the five interference types in ISAC—clutter, self-interference, inter-target, inter-user, and inter-functionality—are best managed by choosing how much interference to avoid, treat as noise, or decode, which is exactly what MA schemes do. The paper walks through OMA-assisted orthogonal ISAC, SDMA/NOMA/RSMA-assisted non-orthogonal ISAC in downlink and uplink, and code-domain and delay-Doppler-domain variants, and shows numerically that RSMA-assisted ISAC delivers the best communication–sensing trade-offs in the scenarios tested. The reason to care: ISAC is a likely 6G feature, and if RSMA indeed unifies the other schemes (reducing to SDMA or NOMA by switching streams off) while its common stream doubles as a radar waveform, then a single transceiver design could serve both functionalities without dedicated radar sequences. The paper frames RSMA as the seed of a unified MA (UMA) framework for future ISAC systems.

What carries the argument

The common stream of one-layer RSMA: each user's message is split into a common sub-message and a private sub-message; a single common stream is jointly encoded and decoded by every user before SIC removes it, while private streams are decoded only by their intended users with residual interference treated as noise. In an ISAC transmitter this same common stream plays three roles at once—managing inter-user interference, absorbing sensing-to-communication interference that would otherwise come from dedicated radar sequences, and contributing to the transmit beampattern that radar needs—so the precoder can be optimized jointly for communication rate and sensing Cramér–Rao bound. RSMA's status

What would settle it

Re-run the WSR/CRB and MFR/CRB trade-off optimizations with NOMA's decoding order jointly optimized (or exhaustively searched) and with information-embedded radar sequences enabled, in the same overloaded and multi-target settings of Figs. 8–10; if NOMA-assisted or SDMA-assisted NO-ISAC matches or exceeds RSMA in any of those regimes, the claim that RSMA 'consistently' delivers the best performance is refuted for that regime.

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Extended reading notes

Core claim

On the paper's own terms, the central claim is that multiple access techniques are the right lens for ISAC interference management, and among them RSMA is the strongest: in the simulation study of Section VII-C, RSMA-assisted ISAC consistently yields the best weighted-sum-rate and max-min-fairness versus Cramér–Rao-bound trade-offs relative to SDMA- and NOMA-assisted ISAC, in both underloaded and overloaded settings and with one or multiple sensing targets. The reason offered is that RSMA splits each user message into a common part and a private part: the common stream is decoded by all users (so part of the interference is decoded, part is treated as noise), and in ISAC that same common str

Load-bearing premise

The comparative claim rests on the simulation protocol of Section VII-C, where NOMA is assigned a fixed decoding order (ascending channel strengths) while RSMA and SDMA precoders are fully optimized, and where additional radar sequences are omitted in the point-target scenario; if NOMA received its optimal decoding order and radar-sequence-based mechanisms were included, the reported RSMA advantage could shrink substantially.

Editorial extensions

If this is right

  • RSMA-assisted ISAC can serve as a single flexible framework: switching off the common stream recovers SDMA, and pushing toward full interference decoding approaches NOMA, so one precoding design covers the whole range of interference levels.
  • The common stream can be shaped to meet radar beampattern requirements, meaning dedicated radar sequences may become unnecessary, simplifying the ISAC transmitter.
  • RSMA-assisted NO-ISAC supports overloaded networks (more users than transmit antennas) and can sense more targets without sacrificing communication QoS, as shown in the multi-target simulations.
  • RSMA's partial-decoding strategy remains beneficial at medium interference levels, where treating interference purely as noise (SDMA) or decoding it fully (NOMA) is suboptimal.
  • The comparisons of Figs. 7–11 imply that OMA-assisted O-ISAC, despite its simplicity and low interference, falls behind non-orthogonal designs in both spectral efficiency and sensing accuracy.

Reading between the lines

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

  • The reported RSMA advantage could narrow under a fully fair comparison: the simulation hands NOMA a fixed decoding order (ascending channel strengths) while RSMA and SDMA precoders are fully optimized by SCA, and omits radar-sequence-enabled designs in which NOMA could embed information in the sensing waveform; optimizing those degrees of freedom is a direct way to test how much of the gap is intr
  • RSMA's advantage should be largest at intermediate interference levels—where neither treating interference as noise nor decoding it entirely is optimal—so simulations sweeping channel correlation and SINR continuously would map exactly where the gain lives.
  • If the common stream doubles as a sensing waveform, then the rate carried by the common stream is tied to radar beampattern quality; jointly optimizing common-rate allocation and beampattern MSE could open a trade-off axis the paper describes but does not fully explore.
  • The UMA argument implies that unifying code-domain NOMA (SCMA/LDS-CDMA) with RSMA remains an open problem, so a natural next step is a scheme whose sparse codewords are also rate-split streams.
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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

3 major / 5 minor

Summary. This paper is a tutorial on multiple access (MA) techniques for interference management in integrated sensing and communication (ISAC). It proposes a taxonomy of ISAC systems (O-ISAC, S-ISAC, NO-ISAC), classifies five types of interference, and reviews OMA, SDMA, NOMA, and RSMA in both downlink and uplink NO-ISAC, along with code-domain and delay-Doppler-domain MA variants. Section VII presents a numerical comparison using WSR/MFR and CRB metrics, concluding that RSMA-assisted ISAC consistently outperforms SDMA- and NOMA-assisted ISAC. The paper closes with applications, open challenges, and research directions.

Significance. The tutorial fills a real gap: it organizes the ISAC interference landscape from a multiple-access perspective and provides a systematic comparison of MA schemes in a common framework. The system models, SINR expressions (Eqs. (4), (5), (7)), FIM/CRB formulas, and rate definitions are standard and correctly assembled. The taxonomy of O-ISAC/S-ISAC/NO-ISAC and the five interference types is useful and well referenced. However, the numerical comparison in Section VII-C is the sole basis for the headline claim that 'RSMA consistently delivers the best performance,' and that comparison is not yet fair: the NOMA baseline is run with a fixed decoding order while RSMA/SDMA precoders are optimized, and radar-sequence-enabled designs—which Sections IV and V identify as key NOMA and RSMA mechanisms—are omitted. The conceptual contributions survive this issue, but the comparative claim is stronger than the current protocol supports.

major comments (3)
  1. [§VII-C, after Eq. (21)] The comparison fixes NOMA's decoding order to 'ascending channel strengths' to reduce complexity, while RSMA and SDMA precoders are optimized via the SCA algorithm from [110]. Section IV-B explicitly states that the decoding order has 'vital impact on performance enhancement' and considers all possible orders. In overloaded and non-aligned channel scenarios (Figs. 8–10), a fixed order can materially understate NOMA's achievable rates. Please re-run the comparison with an optimized or searched decoding order for NOMA, or provide evidence that the reported gap is insensitive to this choice.
  2. [§VII-C, radar-sequence omission] The paper states that 'the additional radar sequences are omitted in this point-target scenario' because K information streams provide enough sensing DoF. This removes the NOMA information-embedding mechanism described in §IV-B and the dedicated sensing beam available to SDMA/RSMA in §IV-A. Thus Figs. 7–11 compare only radar-sequence-disabled designs and cannot support the unqualified claim that 'RSMA-assisted ISAC systems consistently deliver the best performance compared to SDMA and NOMA-assisted ones.' Either include radar-sequence-enabled variants (with and without SIC) for all schemes, or explicitly restrict the claim to radar-sequence-disabled point-target scenarios.
  3. [§VII-C, algorithm implementation] The SCA algorithm from [110] is used 'directly' for all three MA schemes, but the paper does not specify how the NOMA decoding-order constraint is incorporated into the optimization, nor the initialization, convergence tolerance, or number of iterations. Because the numerical conclusion is load-bearing, these implementation details are necessary for reproducibility and for confidence that the comparison is not an artifact of tuning. Please provide the full algorithm specification or a link to reproducible code.
minor comments (5)
  1. [§VII-C opening] Typo: 'momo-static' should be 'mono-static'.
  2. [§VI-B] Typo: 'roubtness' should be 'robustness'.
  3. [§IV-A] Typo: 'rader-sequence-enabled' should be 'radar-sequence-enabled'.
  4. [§IV-B] Minor language: 'quests for' should be 'requires' or 'calls for'.
  5. [§VII-C, Figs. 7–11] The text says RSMA 'consistently' achieves the best performance, but Fig. 7 and the surrounding discussion show SDMA achieving comparable MFR in the communication-prior regime. Consider using 'never worse than' or 'achieves the best trade-off region' to be precise.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the survey's taxonomy and the RSMA-superset discussion are model identities, and the RSMA-superiority claims come from self-contained simulations with acknowledged protocol limitations rather than from a derivation that reduces to its own inputs.

full rationale

This is a tutorial/survey rather than a paper with a new mathematical derivation, so most of its content is classification and literature synthesis. The statement in Sec. VI-C that RSMA 'serves as a superset that includes other MA techniques' and reduces to SDMA/NOMA by switching off streams is a genuine property of the RSMA signal model (common and private streams can be disabled to recover the other schemes), not a circular inference: it is an identity by construction, and the paper does not present it as a derived prediction. The only candidate for circularity is the performance comparison in Sec. VII-C. There, the paper applies the SCA algorithm from [110], a co-authored prior paper, and adopts two protocol choices: 'to reduce computational complexity, we directly adopt a specific decoding order according to the ascending channel strengths in NOMA-assisted ISAC' and 'The additional radar sequences are omitted in this point-target scenario.' These are explicitly acknowledged limitations, and they raise a legitimate fairness concern about whether the NOMA baseline and the radar-sequence-enabled designs are given their strongest possible configurations before the claim 'RSMA-assisted ISAC systems consistently deliver the best performance' is made. However, that is a correctness/fairness concern, not circularity: the headline is an empirical output of the simulations, not a quantity fitted from a subset of the data and then renamed a prediction, and it is not forced by the equations. The self-citation to [110] is a citation to an algorithmic tool used in the comparison, not a load-bearing appeal to an unverified result, uniqueness theorem, or ansatz. The paper's conceptual contributions—the interference taxonomy, the O/S/NO-ISAC categorization, and the qualitative comparison of MA-assisted designs—do not depend on the numerical comparison. Under the hard rule that circularity must be demonstrated by a specific reduction (e.g., Eq. X equals Eq. Y by construction, or a fitted parameter is renamed as a prediction), no such reduction can be exhibited here. Therefore the appropriate finding is no significant circularity.

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

The ledger shows the numerical comparison is built on hand-chosen trade-off parameters (phi, rho), a handicapping protocol choice for NOMA, and simplifications (perfect CSI, no clutter, no radar sequences) that systematically favor the flexible partial-decoding narrative. The expository sections inherit standard ISAC/MA modeling assumptions from the cited literature, which is normal for a tutorial. No new physical entities are introduced.

free parameters (4)
  • phi (trade-off regularization in problem (21a)) = not reported; swept to trace trade-off curves in Figs. 7-11
    Weights WSR/MFR against the CRB eigenvalue bound. The sweep schedule and spacing are hand-chosen and unreported, which shapes the trade-off regions shown.
  • rho (O-ISAC communication time fraction) = not reported; varied per curve in Figs. 7-9
    O-ISAC curves are generated by giving rho of time to communication and 1-rho to sensing; values are not listed, so the O-ISAC baseline is not independently reproducible.
  • NOMA decoding order (ascending channel strengths) = fixed heuristic order
    A protocol choice in Section VII-C constraining NOMA to a single decoding order without joint optimization, while RSMA/SDMA are fully optimized; this handicaps the NOMA baseline and inflates the reported gap.
  • Simulation constants (PT, sigma_c^2, sigma_r^2, I, |alpha_q|, target angles/velocities) = Table V values
    Hand-chosen benchmark settings; standard practice and not fitted to data, but they are the only specification of the numerical study and are not accompanied by code.
assumptions (4)
  • domain assumption Perfect CSI at the BS and all communication users (Section IV preamble)
    All SINR expressions (4), (5), (7) and the comparisons assume perfect CSI; robustness to CSIT imperfection is discussed only qualitatively for RSMA.
  • domain assumption Point-target radar model without clutter in the numerical comparison
    Clutter is one of the five taxonomized interference types but is absent from the model underlying Figs. 7-11, so the comparative claims do not cover clutter-limited regimes.
  • ad hoc to paper Radar sequences are omitted because K information streams provide enough sensing DoF (Section VII-C)
    Excludes radar-sequence-enabled designs, the mechanism by which NOMA (information embedding) and RSMA (common stream as radar sequence) manage sensing-to-communication interference in Sections IV-V; the tested systems are stripped of their distinguishing interference-management features.
  • standard math The SCA algorithm of [110] converges to a stationary point of (21)
    Invoked by citation only; no convergence analysis, iteration details, or complexity statement appears in this paper, and [110] is a co-authored prior work.

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Pith. "Pith review of Interference Management for Integrated Sensing and Communications: A Multiple Access Perspective." pith.science (2026). https://pith.science/paper/27J4U5AJ

@misc{pith2026250902352,
  author       = {Pith},
  title        = {Pith review of: Interference Management for Integrated Sensing and Communications: A Multiple Access Perspective},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/27J4U5AJ}},
  note         = {Machine review of arXiv:2509.02352}
}
read the original abstract

The integrated sensing and communication (ISAC) technique has been considered a key enabler for 6G radio access networks. ISAC fulfills a brand new paradigm shift in wireless networks via the seamless interplay between communication and sensing within a unified network. However, the tight integration of these functionalities inevitably gives rise to various types of interference, posing significant challenges to existing ISAC waveform designs and rendering interference management a critical concern. Inspired by the development trajectory of wireless communications, different multiple access (MA) techniques, such as orthogonal multiple access (OMA), space-division multiple access (SDMA), and more recently, non-orthogonal multiple access (NOMA) and rate-splitting multiple access (RSMA), have been demonstrated to play a pivotal role in efficiently utilizing limited spectrum resources, designing ISAC waveforms, as well as managing inter-user interference and inter-functionality interference in ISAC. Notably, the interplay between MA and ISAC presents mutually beneficial integration. On the one hand, ISAC helps MA techniques better exploit their interference management capability beyond the communication-only networks. On the other hand, different MA techniques serve as promising solutions for inter-functionality and inter-user interference management in ISAC. In this paper, we deliver the first comprehensive tutorial of MA techniques in ISAC networks. Specifically, we illustrate the fundamental principles of ISAC, classify the diverse types of interference in different ISAC systems, and compare MA-assisted ISAC designs, highlighting their respective advantages and limitations. Moreover, we provide an outlook on the emerging applications and future research directions of different MA-assisted ISAC.

Figures

Figures reproduced from arXiv: 2509.02352 by the authors.

Figure 1
Figure 1. The typical O-ISAC and NO-ISAC models. which thereby enables maximum integration by exploiting the same signal. It is worth noting that different dual-functional waveform designs result in varying types of interference in certain NO-ISAC architectures, which will be discussed in detail later. In the following, we outline the five distinct NO￾ISAC architectures and the specific interference challenges associated with… view at source ↗
Figure 2
Figure 2. Various types of interference observed in five distinct NO-ISAC architectures. [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Typical three schemes of joint dual-functional waveform design in [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Typical O-ISAC systems assisted by OMA in the downlink or uplink. [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]
Figure 5
Figure 5. Figure 5: Non-orthogonal integrated downlink communication and downlink [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: Non-orthogonal integrated uplink communication and downlink mono [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]
Figure 9
Figure 9. Figure 9: WSR versus tr(CRB). NT = 4, NR = 5, K = 3, Q = 1, µ = [0.4, 0.3, 0.3]. 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 0.055 average tr(CRB) 0.5 1 1.5 2 2.5 3 3.5 4 4.5 MFR (bps/Hz) Q=1;RSMA Q=1;SDMA Q=1;NOMA Q=2;RSMA Q=2;SDMA Q=2;NOMA Q=3;RSMA Q=3;SDMA Q=3;NOMA [PITH_FUL…
Figure 10
Figure 10. Figure 10: MFR versus average tr(CRB) under different numbers of targets. [PITH_FULL_IMAGE:figures/full_fig_p023_10.png]
Figure 11
Figure 11. Figure 11: MFR versus tr(CRB) under various angle differences. [PITH_FULL_IMAGE:figures/full_fig_p024_11.png]
Figure 12
Figure 12. Figure 12: A few applications of MA-ISAC in next-generation wireless networks. [PITH_FULL_IMAGE:figures/full_fig_p025_12.png]

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Pith tools

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