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REVIEW 4 major objections 5 minor 27 references

RIS-Assisted Near-Field ISAC for Multi-Target Indication in NLoS Scenarios

T0 review · 4 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read RIS-assisted near-field beamforming can separate co-directional targets even when line-of-sight paths are blocked, by extending far-field sensing metrics to a joint angle-and-distance space and jointly optimizing base-station covariance wit

desk verdict Sensible near-field RIS-ISAC formulation, but the SDR for the RIS phase subproblem is algebraically wrong and the algorithm doesn't solve the problem it claims. read the letter →

arxiv 2509.08642 v1 pith:6PB2ITQC submitted 2025-09-10 eess.SP

Hang Ruan (1) , Homa Nikbakht (1 , 2) , Ruizhi Zhang (1 , 3) , Honglei Chen (4) , Yonina C. Eldar (1) ((1) Weizmann Institute of Science , (2) Princeton University
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(3) University of Electronic Science Technology of China (4) MathWorks Inc.)
This is my paper · ORCID
classification eess.SP
keywords integratedsensingandcommunicationreconfigurableintelligentsurfacenear-fieldbeamformingmulti-targetnon-line-of-sightsemidefiniterelaxationcross-correlationsuppressionbeampatterngain
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 establish that a reconfigurable intelligent surface (RIS) lets a near-field integrated sensing and communication (ISAC) system indicate multiple targets even when direct paths are blocked, including targets lying along the same direction. The core move is to extend two classic MIMO-radar metrics, beampattern gain and inter-target cross-correlation, from the far field into the near field, where the spherical wavefront adds a usable distance dimension. The authors formulate a joint optimization that maximizes the worst-case weighted beampattern gain while suppressing cross-correlation between targets and preserving per-user communication rates, then solve it by alternating optimization with semidefinite relaxation. Simulations show that the design separates NLoS targets in the same direction to the RIS and creates sharp SINR peaks at NLoS user locations. A sympathetic reader would take the contribution as evidence that near-field distance information, routed around blockages by a RIS, is enough to resolve targets that far-field angle-only designs cannot.

What carries the argument

The central object is the affine channel representation h_{t,a}(φ) = α_a h̄_{t,a} + A_a φ, where φ collects the RIS phase shifts, combined with the augmented matrix Ψ̄ = [φ; 1][φ; 1]^H. This turns every quadratic beampattern, cross-correlation, and SINR term into a linear trace of Ψ̄ with a constructed matrix M, so the RIS subproblem becomes a convex semidefinite program after relaxing rank-one and unit-modulus constraints. The BS subproblem uses a separate SDR that lifts each beamforming vector into a covariance matrix and then recovers a rank-one beamformer using a known construction that preserves the transmit covariance, sensing constraints, and rate constraints.

What would settle it

Run the proposed alternating algorithm on a random near-field channel instance, compute the projected RIS phase vector, and directly evaluate constraints (18b)-(18d); if the projected vector violates the cross-correlation or beampattern constraints while the relaxed SDP satisfies them, the simulated performance claim does not carry a feasibility guarantee. A second check is to compare the Capon spectrum peaks against the true target positions across many blockage geometries to confirm that the three distinct peaks are not a particular configuration artifact.

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Extended reading notes

Core claim

The paper's central claim is that a RIS-assisted near-field ISAC system can support multi-target indication in non-line-of-sight scenarios, including targets that are co-directional relative to the RIS, provided the transmit covariance and RIS phase shifts are jointly optimized. The sensing objective is to maximize the minimum weighted beampattern gain across targets, subject to each target's gain being at least that level, the magnitude of cross-correlation between any target pair being a small fraction of it, per-user rate constraints, and total power. The novelty is twofold: the explicit cross-correlation-suppression constraint, borrowed from MIMO radar probing design, keeps target echoes

Load-bearing premise

The claim relies on the unproven step that the RIS phase vector recovered by taking the relaxed SDP's dominant eigenvector, normalizing it, and projecting each phase to unit modulus still satisfies the beampattern, cross-correlation, and rate constraints; the proof covers only the relaxed matrix, not this projected vector.

Editorial extensions

If this is right

  • If the framework holds, near-field ISAC can resolve multiple targets in the same angular direction whenever they differ in distance, something far-field angle-only designs cannot do.
  • Explicit cross-correlation suppression is presented as necessary for multi-target indication: without it, the Capon spectrum smears and produces ambiguous peaks even when the near-field model is used.
  • A RIS can create virtual LoS links for blocked users and targets, so NLoS scenarios become serviceable without changing the base-station deployment.
  • The design is agnostic to target radar cross-sections, which makes it applicable when target RCS is unknown, unlike SINR- or MI-based sensing metrics.
  • The alternating SDR algorithm gives a tractable path to jointly satisfy communication rate guarantees and sensing objectives in one transmit signal.

Reading between the lines

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

  • A natural extension would be to test the same angle-range separation idea with hybrid analog-digital beamforming or sparse arrays, where the near-field distance resolution could be traded against hardware complexity.
  • Because the RCS-agnostic metric depends only on channel geometry, the same formulation might apply to passive or non-cooperative targets without needing target-specific calibration.
  • The unverified step is the rank-one recovery in Section 3.3: the theoretical guarantees apply to the relaxed SDP, not to the projected phase vector, so a direct constraint-margin check on random channel realizations would quantify whether the heuristic ever loses feasibility.
  • The distance-resolution capability suggests that increasing array aperture or carrier frequency improves co-angle target separation, but this scaling is not explicitly quantified in the paper and would be a natural next calculation.
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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. The paper proposes a joint BS transmit beamforming and RIS phase-shift design for near-field ISAC systems with multiple users and targets under NLoS blockage. The key idea is to extend far-field beampattern gain and inter-target cross-correlation metrics to the near-field, enabling discrimination of co-directional targets by exploiting the distance dimension. Problem (13) maximizes the worst-case weighted beampattern gain subject to cross-correlation suppression, per-user rate constraints, and a total power constraint. The authors develop an alternating optimization algorithm: for fixed RIS phases, the BS covariance is optimized via an SDR (problem (15)) with a closed-form rank-one reconstruction; for fixed BS covariance, the RIS phase vector is optimized via another SDR (problem (18)) followed by a heuristic dominant-eigenvector unit-modulus projection. Simulations present SINR maps and a Capon spectrum for a specific 7x7 array geometry. The central conclusion is that the proposed near-field, cross-correlation-aware design resolves co-angle targets in blocked scenarios, whereas far-field and no-suppression benchmarks fail.

Significance. If the formulation and algorithm were correct, the paper would make a useful contribution to near-field RIS-assisted ISAC: it extends two classical MIMO-radar sensing metrics to the near-field with RIS-aided NLoS links, and it demonstrates via a concrete example that explicit cross-correlation suppression is important for multi-target indication. The AO/SDR structure follows established practice, and the problem formulation is sensible. However, the paper's central algorithmic step contains a load-bearing algebraic error in the quadratic-form representation of the sensing metrics (Section 3.3), and the rank-one recovery is heuristic with no feasibility certificate. These issues undermine the claimed equivalence between the relaxed SDP and the original problem, and therefore also the simulation-based conclusions. The work also provides no code or reproducibility artifacts, and the sensing evaluation is a single scenario without quantitative metrics. The significance of the paper is conditional on fixing the SDP formulation and validating the heuristic recovery.

major comments (4)
  1. [Section 3.3, matrix M_{l,l'} definition] The matrix M_{l,l'} used to express the beampattern and cross-correlation terms as Tr(M_{l,l'} Psi) is not the correct quadratic-form matrix for h_{t,l}(phi)^T R_x h_{t,l'}(phi)^*. The top-left block A_l^H R_x^T A_{l'} equals (A_l^T R_x A_{l'}^*)^*, and the top-right block A_l^H R_x^T (alpha_{l'} hbar_{t,l'}) equals (A_l^T R_x (alpha_{l'} hbar_{t,l'})^*)^*, while the bottom row is not conjugated. Consequently Tr(M_{l,l'} Psi) equals the desired expression with its first two terms conjugated. For complex channels this is not even real when l=l'. A scalar check: N_t=N_s=1, R_x=1, hbar=1, A=j, phi=1 gives h^T R_x h^* = 2 but the printed M yields Tr(M Psi)=2-2j. Thus constraint (18b) is ill-posed (a complex number cannot be >= mu) and (18c) does not enforce the stated metric. The SDP (18) optimizes a different problem than (13), and the later rank-one recovery cannot repair this mismatch. Th
  2. [Section 3.3, rank-one recovery] After solving the relaxed SDP (18), the paper recovers a physically valid RIS phase vector by taking the dominant eigenvector of Psi^*, normalizing by its last entry, and projecting each phase to unit modulus. No argument is given that this projection preserves feasibility of constraints (18b)-(18d). Since (18b)-(18d) are nonconvex in phi, the projected vector may violate the beampattern, cross-correlation, or SINR guarantees. The theoretical claims of the paper therefore hold only for the relaxed SDP, while the simulated performance rests on an unverified heuristic. The authors should either provide a feasibility guarantee, use a randomized rounding with feasibility repair, or at minimum report the empirical feasibility rate of the projection across many problem instances.
  3. [Problem (13) and simulation parameters] The weights w_l and w_{l,l'} appear in constraints (13b)-(13c) but are never defined. Their values directly affect the trade-off between beampattern gain and cross-correlation suppression, and hence the reported Capon spectrum. Similarly, the cross-correlation tolerance epsilon is set to 0.1 in the simulation but its influence on resolution is not studied. Without specifying how w_l and w_{l,l'} are chosen (e.g., equal weights, or normalized by path loss), the problem is incompletely specified and the simulation results are not reproducible.
  4. [Section 4.3, Fig. 3] The sensing performance is evaluated with a single Capon spectrum for one fixed geometry. No quantitative metric (e.g., detection probability, RMSE of target locations, peak-to-sidelobe ratio, or resolution success rate over Monte Carlo trials) is reported. The claim that the proposed method 'enables high-resolution sensing of co-angle targets' is not substantiated by a single spectral image. I recommend adding quantitative performance measures and a parameter sweep (e.g., over epsilon, SNR, or target separation) to support the central claim.
minor comments (5)
  1. [Abstract/Introduction] Typographical issues: 'This approach is offers two distinct advantages' (Introduction, Section 1) should be 'This approach offers'.
  2. [Section 2.4] The line defining hbar_{l,r} reads 'the target-to-BS LoS channel hbar_l,r C N_r'; it should be 'hbar_l,r in C^{N_r}'.
  3. [Section 3.1, Eq. (13)] The text says constraints (13f) and (13g) are 'repetitions of (1) and (6)' but (13f) also includes the sum of f_k f_k^H and R_s, which is indeed (1). This wording is confusing; please rewrite.
  4. [Section 4.3, Fig. 3] The Capon spectrum is computed with the same near-field channel model used in the design. An independent validation (e.g., a full-wave simulator or different channel approximation) would increase confidence that the resolution gain is not an artifact of model self-consistency.
  5. [References] Reference [15] is an arXiv preprint. If a published version exists, please cite it; if not, indicate its status clearly.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the design objective and the evaluation metric are not built from the claimed outcome; self-citations are attribution only.

full rationale

The paper's derivation chain is self-contained for the purposes of circularity analysis. The sensing metrics in Section 2.4 are defined directly as quadratic forms h_{t,l}(Φ)^T R_x h_{t,l'}(Φ)^* of the near-field channel vectors given in (2)-(7). The optimization problem (13) then maximizes/minimizes exactly these quantities. This is a direct objective definition, not a fitted-target equivalence: no parameter is fitted to the Capon spectrum output or to the target locations. The near-field extension is attributed to the authors' prior work [15], but the present paper re-derives the channel expressions explicitly, and the metric is written out in full in (13b)-(13c). Thus the self-citation is provenance, not a load-bearing unverified premise. The proof of the beamformer construction (16) cites [20, Theorem 1]; even though Y.C. Eldar is a co-author of that reference, the cited theorem is a published external result about rank-one beamforming, not an imported conclusion equivalent to this paper's own claim. The rank-one projection after SDP (18) is heuristic and the paper does not prove feasibility of the projected solution; that is a correctness/feasibility limitation, not circularity. Likewise, the M_{l,l'} conjugation issue flagged by the skeptic is a correctness concern about whether the printed SDP (18) matches (13), not a circular reduction: the relaxed problem and the original problem differ due to an algebraic error, not because the output was baked into the input. Simulations use an independent Capon spectrum estimator on synthetic channel realizations, and the benchmark comparisons are not constructed to force the proposed method's success. No 'prediction' in the paper reduces to a fitted input or to a self-citation chain.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The ledger is light: the paper builds on existing optimization machinery. The main load-bearing choices are ideal near-field and RIS modeling, arbitrarily chosen weights, and an unproved rank-one recovery heuristic.

free parameters (2)
  • w_l and w_{l,l'} weights = not specified
    Appear in constraints (13b)-(13c) to weight beampattern and cross-correlation terms, but the paper never defines them or gives simulation values; they shape the objective and feasibility trade-off.
  • cross-correlation tolerance epsilon = 0.1
    Set by hand in Section 4.1; it controls how much inter-target correlation is allowed and is not varied in a sensitivity study.
assumptions (5)
  • domain assumption Near-field spherical-wave channel model with perfect knowledge of all user and target positions and channels.
    Invoked in Section 2 to define h_{t,k} and h_{t,l}; the whole design and the Capon spectrum use these channels, so model mismatch or position error would break the claimed resolution.
  • domain assumption The RIS is an ideal phase-only diagonal reflector with no mutual coupling, amplitude loss, or hardware impairments.
    Equation (6) models Phi as diag(e^{j theta}); all results assume this ideal response.
  • ad hoc to paper Rank-one recovery of the RIS phases from the SDR by dominant eigenvector, normalization, and unit-modulus projection yields a feasible solution.
    Section 3.3 after (18); no proof that the projected phase vector satisfies (18b)-(18d).
  • domain assumption Alternating optimization converges to a meaningful fixed point.
    The AO loop is stated in Section 3.1, but no convergence or stationarity proof is given; performance is shown only for one simulation run.
  • standard math Use of Liu et al. [20, Theorem 1] for beamformer extraction.
    Relied on in Section 3.2 to construct a rank-one f_k from F_k; accepted external result, not proved here.

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Cite this review

Pith. "Pith review of RIS-Assisted Near-Field ISAC for Multi-Target Indication in NLoS Scenarios." pith.science (2026). https://pith.science/paper/6PB2ITQC

@misc{pith2026250908642,
  author       = {Pith},
  title        = {Pith review of: RIS-Assisted Near-Field ISAC for Multi-Target Indication in NLoS Scenarios},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6PB2ITQC}},
  note         = {Machine review of arXiv:2509.08642}
}
read the original abstract

Enabling multi-target sensing in near-field integrated sensing and communication (ISAC) systems is a key challenge, particularly when line-of-sight paths are blocked. This paper proposes a beamforming framework that leverages a reconfigurable intelligent surface (RIS) to achieve multi-target indication. Our contribution is the extension of classic beampattern gain and inter-target cross-correlation metrics to the near-field, leveraging both angle and distance information to discriminate between multiple users and targets. We formulate a problem to maximize the worst-case sensing performance by jointly designing the beamforming at the base station and the phase shifts at the RIS, while guaranteeing communication rates. The non-convex problem is solved via an efficient alternating optimization (AO) algorithm that utilizes semidefinite relaxation (SDR). Simulations demonstrate that our RIS-assisted framework enables high-resolution sensing of co-angle targets in blocked scenarios.

Figures

Figures reproduced from arXiv: 2509.08642 by the authors.

Figure 1
Figure 1. Illustration of the RIS-assisted near-field ISAC. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. SINR distribution for communications users. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Normalized Capon spectrum for sensing, comparing the [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

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Works this paper leans on

27 extracted references · 2 linked inside Pith

  1. [1]

    By sharing spectrum and hardware, ISAC improves spectral efficiency, reduces costs, and cre- ates synergy between the two functions [1]

    INTRODUCTION Future 6G networks require high-accuracy sensing alongside high- speed communication, for which integrated sensing and communi- cation (ISAC) is a pivotal technology [1]. By sharing spectrum and hardware, ISAC improves spectral efficiency, reduces costs, and cre- ates synergy between the two functions [1]. The trend towards large antenna arra...

  2. [2]

    System Model We consider a near-field mmWave ISAC system (Fig

    SYSTEM AND SIGNAL MODEL 2.1. System Model We consider a near-field mmWave ISAC system (Fig. 1) compris- ing a base station (BS), an RIS,Ksingle-antenna users in the set K, andLpoint-targets in the setL. The BS is equipped with an Nt-element transmit array and anN r-element receive array, with antenna positionsp t(nt),p r(nr)∈R 3 and corresponding normal v...

    arXiv 2025

  3. [3]

    JOINT BEAMFORMING AND RIS DESIGN 3.1. Optimization formulation We aim to jointly design the BS transmit beamformers{f k}, the sensing covarianceR s, and the RIS phase shiftsΦto optimize the performance of ISAC system, which is formulated as follows: max {fk},Rs⪰0, Rx,Φ,µ,{θns } µ(13a) s.t.w l hT t,l(Φ)Rxh∗ t,l(Φ) ≥µ,∀l∈ L,(13b) wl,l′ hT t,l(Φ)Rxh∗ t,l′ (Φ...

  4. [4]

    SIMULATION RESULTS In this section, we evaluate the performance of our proposed joint beamforming and RIS optimization framework. We present numer- ical results for a near-field scenario with communication users and sensing targets in NLoS positions, demonstrating the effectiveness of the RIS in establishing virtual links for multiple users and targets. 4...

  5. [5]

    CONCLUSION In this paper, we addressed the critical challenge of LoS blockage in near-field multi-target ISAC systems. We formulated a joint trans- mission beamforming and RIS phase shifts optimization problem to maximize sensing performance while guaranteeing communication quality and suppressing interference. An efficient AO algorithm was developed to s...

  6. [6]

    101000967), by the Israel Science Foundation (grant No

    ACKNOWLEDGEMENTS This research was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and in- novation program (grant No. 101000967), by the Israel Science Foundation (grant No. 536/22), and by the Manya Igel Centre for Biomedical Engineering and Signal Processing

    2020

  7. [7]

    Integrated sensing and communications: To- ward dual-functional wireless networks for 6G and beyond,

    F. Liu, Y . Cui, C. Masouros, J. Xu, T. X. Han, Y . C. Eldar, and S. Buzzi, “Integrated sensing and communications: To- ward dual-functional wireless networks for 6G and beyond,” IEEE J. Sel. Areas Commun., vol. 40, no. 6, pp. 1728–1767, 2022

    2022

  8. [8]

    Near-field integrated sensing and communications,

    Z. Wang, X. Mu, and Y . Liu, “Near-field integrated sensing and communications,”IEEE Commun. Lett., vol. 27, no. 8, pp. 2048–2052, 2023

Show all 27 references
  1. [9]

    Near-field ISAC: Beamforming for multi-target detection,

    D. Galappaththige, S. Zargari, C. Tellambura, and G. Y . Li, “Near-field ISAC: Beamforming for multi-target detection,” IEEE Wireless Commun. Lett., 2024

    2024

  2. [10]

    In- tegrated sensing and communication with reconfigurable in- telligent surfaces: Opportunities, applications, and future di- rections,

    R. Liu, M. Li, H. Luo, Q. Liu, and A. L. Swindlehurst, “In- tegrated sensing and communication with reconfigurable in- telligent surfaces: Opportunities, applications, and future di- rections,”IEEE Wireless Commun., vol. 30, no. 1, pp. 50–57, 2023

    2023

  3. [11]

    Semi-passive RIS aided near-field ISAC: CRB analysis and optimization,

    X. Dong, Z. Fei, M. Hua, X. Wang, and Y . Liu, “Semi-passive RIS aided near-field ISAC: CRB analysis and optimization,” IEEE Trans. V eh. Technol., vol. 74, no. 6, pp. 9084–9099, 2025

    2025

  4. [12]

    Joint design of trajectory and near-field beamforming for STAR-RIS-Enabled ISAC,

    Q. Huang, Z. Song, Z. Xiong, G. Xu, N. Zhao, and D. Niy- ato, “Joint design of trajectory and near-field beamforming for STAR-RIS-Enabled ISAC,” inProc. Int. Conf. Wireless Com- mun. Signal Process. (WCSP), 2024, pp. 175–180

    2024

  5. [13]

    Near- field extremely large-scale STAR-RIS enabled integrated sens- ing and communications,

    J. Zhou, Y . Yang, Z. Yang, and M. R. Shikh-Bahaei, “Near- field extremely large-scale STAR-RIS enabled integrated sens- ing and communications,”IEEE Trans. Green Commun. Netw., vol. 9, no. 1, pp. 404–416, 2025

    2025

  6. [14]

    Near-field ISAC for a RIS-assisted system,

    N. Xue, X. Mu, Y . Chen, and Y . Liu, “Near-field ISAC for a RIS-assisted system,” inProc. IEEE Global Commun. Conf. (GLOBECOM), 2024, pp. 4034–4039

    2024

  7. [15]

    RIS- assisted near-field integrated sensing and symbiotic radio sys- tems,

    Z. Zhu, M. Ning, G. Sun, Q. Guo, Z. Chu, and I. Lee, “RIS- assisted near-field integrated sensing and symbiotic radio sys- tems,” inProc. IEEE Int. Conf. Commun. Technol. (ICCT), 2024, pp. 1697–1701

    2024

  8. [16]

    Intelli- gent omni surfaces assisted integrated multi-target sensing and multi-user MIMO communications,

    Z. Zhang, W. Chen, Q. Wu, Z. Li, X. Zhu, and J. Yuan, “Intelli- gent omni surfaces assisted integrated multi-target sensing and multi-user MIMO communications,”IEEE Trans. Commun., vol. 72, no. 8, pp. 4591–4606, 2024

    2024

  9. [17]

    Joint beamforming design for the STAR-RIS-enabled ISAC systems with multiple targets and multiple users,

    S. Zhang, W. Hao, G. Sun, Z. Zhu, X. Li, and Q. Wu, “Joint beamforming design for the STAR-RIS-enabled ISAC systems with multiple targets and multiple users,”IEEE Trans. Com- mun., vol. 73, no. 1, pp. 693–708, 2025

    2025

  10. [18]

    RIS-Assisted cooperative multicell ISAC systems: A multi-user and multi- target case,

    X. Yang, Z. Wei, Y . Liu, H. Wu, and Z. Feng, “RIS-Assisted cooperative multicell ISAC systems: A multi-user and multi- target case,”IEEE Trans. Wireless Commun., vol. 23, no. 8, pp. 8683–8699, 2024

    2024

  11. [19]

    Dual- functional MIMO beamforming optimization for RIS-aided in- tegrated sensing and communication,

    X. Zhao, H. Liu, S. Gong, X. Ju, C. Xing, and N. Zhao, “Dual- functional MIMO beamforming optimization for RIS-aided in- tegrated sensing and communication,”IEEE Trans. Commun., vol. 72, no. 9, pp. 5411–5427, 2024

    2024

  12. [20]

    Secure design for RIS-assisted multi-target ISAC sys- tems,

    X. Wang, X. Chen, H. Cao, Z. M. Yetneberk, C. Liu, and T.-X. Zheng, “Secure design for RIS-assisted multi-target ISAC sys- tems,” inProc. Int. Conf. Ubiquitous Commun. (Ucom), 2024, pp. 510–515

    2024

  13. [21]

    Near-field integrated sensing and communication for multi- target indication,

    H. Ruan, H. Nikbakht, R. Zhang, H. Chen, and Y . C. Eldar, “Near-field integrated sensing and communication for multi- target indication,”arXiv preprint arXiv:2506.07052, 2025

    2025 arXiv

  14. [22]

    On probing signal design for MIMO radar,

    P. Stoica, J. Li, and Y . Xie, “On probing signal design for MIMO radar,”IEEE Trans. Signal Process., vol. 55, no. 8, pp. 4151–4161, 2007

    2007

  15. [23]

    Multi- objective optimization-based transmit beamforming for multi- target and multi-user MIMO-ISAC systems,

    C. Meng, Z. Wei, D. Ma, W. Ni, L. Su, and Z. Feng, “Multi- objective optimization-based transmit beamforming for multi- target and multi-user MIMO-ISAC systems,”IEEE Internet Things J., 2024

    2024

  16. [24]

    Optimal transmit beamforming for integrated sensing and communication,

    H. Hua, J. Xu, and T. X. Han, “Optimal transmit beamforming for integrated sensing and communication,”IEEE Trans. V eh. Technol., vol. 72, no. 8, pp. 10 588–10 603, 2023

    2023

  17. [25]

    Beam focusing for near-field multiuser MIMO communications,

    H. Zhang, N. Shlezinger, F. Guidi, D. Dardari, M. F. Imani, and Y . C. Eldar, “Beam focusing for near-field multiuser MIMO communications,”IEEE Trans. Wireless Commun., vol. 21, no. 9, pp. 7476–7490, 2022

    2022

  18. [26]

    Joint transmit beamforming for multiuser MIMO com- munications and MIMO radar,

    X. Liu, T. Huang, N. Shlezinger, Y . Liu, J. Zhou, and Y . C. Eldar, “Joint transmit beamforming for multiuser MIMO com- munications and MIMO radar,”IEEE Trans. Signal Process., vol. 68, pp. 3929–3944, 2020

    2020

  19. [27]

    Near-field integrated sensing and communication with extremely large-scale antenna array,

    H. Hua, J. Xu, and R. Zhang, “Near-field integrated sensing and communication with extremely large-scale antenna array,” IEEE Trans. Wireless Commun., 2025

    2025

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