Pith. sign in

REVIEW 3 major objections 6 minor 41 references

Beamforming and Phase Shift Design for STAR-RIS Assisted Secure Sensing and Communication in ISAC Systems

T0 review · 3 major / 6 minor · reviewed 2026-07-31 · grok-4.5

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A STAR-RIS splits space into communication and sensing halves so one joint design of base-station beams and surface phases can raise secrecy rate while tightening the eavesdropper’s angle CRB.

desk verdict Incremental STAR-RIS secure-ISAC tradeoff paper whose written BCD never actually drives beams or phases by the CRB term, despite the claim and the μ-plots. read the letter →

arxiv 2607.28081 v1 pith:BMVYR3MV submitted 2026-07-30 eess.SP

Haijun Zhang , Shuqing Wu , Xiaoqi Zhang , Zijun Wu , Xu Ma , Yuzheng Ren This is my paper
classification eess.SP
keywords integratedsensingandcommunicationSTAR-RISCramér-Raoboundsecrecyratebeamformingphysical-layersecurityblockcoordinatedescent
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 shows that a simultaneously transmitting and reflecting surface (STAR-RIS) can give an integrated sensing-and-communication base station full-space coverage: users sit on the transmission side while a point target that may eavesdrop sits on the reflection side. Secrecy rate measures how much confidential information stays away from the target; the Cramér-Rao bound (CRB) on the target’s direction-of-arrival measures how accurately the system can locate it. The authors formulate a single weighted objective that trades these two quantities against each other and solve it by alternately updating transmit beamforming and STAR-RIS amplitudes/phases. Simulations indicate the joint design outperforms designs that optimize only the transmitter or only the surface, so a network operator can deliberately dial security versus sensing precision with one set of knobs.

What carries the argument

A hybrid block-coordinate-descent loop: successive convex approximation plus semidefinite relaxation for the beamformers, a penalty-dual-decomposition sub-loop for the CRB auxiliary matrix, and projected gradient ascent on the STAR-RIS phase/amplitude manifold under the energy-splitting constraint.

What would settle it

Move the point target by more than a few degrees inside one coherent interval (or feed the optimizer deliberately outdated angles) and check whether the realized secrecy rate and empirical angle MSE still match the optimized tradeoff curve; a clear collapse would falsify the claim that the design remains effective under the fixed-angle premise.

Watch

Extended reading notes

Core claim

In a STAR-RIS-assisted ISAC system where the sensing target is also the eavesdropper, jointly optimizing the base-station beamforming matrix, sensing covariance and the STAR-RIS transmission/reflection coefficients yields a strictly better secrecy-rate-versus-CRB tradeoff than optimizing either the transmitter or the surface alone.

Load-bearing premise

The eavesdropper’s angles are treated as already known and fixed from the previous coherent block; if those angles are wrong or the target moves inside the block, both the CRB guarantee and the secrecy calculation become mismatched to reality.

Editorial extensions

If this is right

  • Operators can place users and a potential eavesdropper on opposite sides of a single STAR-RIS panel and still serve both functions.
  • Increasing base-station antennas or STAR-RIS elements monotonically improves both secrecy rate and CRB under the joint design.
  • The scalar weight μ gives a continuous knob that trades bits of secrecy rate for tighter angle estimation.
  • The same algorithmic skeleton extends, as the authors note, to multi-target and imperfect-hardware settings.

Reading between the lines

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

  • Because the sensing array is co-located on the STAR-RIS panel, the design already assumes a wired or low-latency backhaul for the echo samples; any practical deployment must budget that link.
  • The fixed-DOA premise suggests a natural outer tracking loop (e.g., a Kalman filter on angles) whose residual error would directly degrade the inner CRB-secrecy tradeoff—an extension left open by the paper.
  • Energy-splitting STAR-RIS hardware that cannot enforce the orthogonal-phase condition of equation (3) would shrink the feasible set and likely flatten the achieved tradeoff surface.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper studies a STAR-RIS-assisted ISAC system in which a multi-antenna BS serves single-antenna users on the transmission side while sensing a point target (treated as an eavesdropper) on the reflection side. Secrecy rate (sum user rate minus aggregate eavesdropper rate) and the CRB of the target’s 2D DOA are adopted as metrics. A weighted tradeoff problem P is formulated over transmit beamformers W, sensing covariance R_s, and STAR-RIS transmission/reflection coefficients. A hybrid BCD algorithm is proposed: SCA+SDR for the beamforming subproblem, a PDD loop for an auxiliary CRB matrix pair (U,F), and projected gradient (Wirtinger) updates for the STAR-RIS phases. Simulations claim superior secrecy–CRB tradeoff versus transmit-only and STAR-RIS-only baselines, plus the expected monotonicities in power, antenna count, and sensors.

Significance. Full-space STAR-RIS coverage for secure ISAC, with the target as eavesdropper, is a timely and practically motivated setting that goes beyond half-space RIS-ISAC. The CRB derivation for the vectorized Gaussian echo model (FIM blocks for (θ1,θ2,β)) follows standard steps and is useful. If the joint algorithm truly realizes the claimed μ-controlled tradeoff, the work would be a solid systems contribution for the eess.SP / ISAC community. The manuscript does not ship code, proofs beyond standard citations, or parameter-free predictions; significance therefore rests entirely on correctness of the optimization procedure and the supporting figures.

major comments (3)
  1. [§IV-B, Eqs. (45), (59), (62), (64)–(67); Alg. 1–3; Fig. 8] The central claim is that hybrid BCD jointly optimizes W, R_s and STAR-RIS coefficients for the weighted secrecy–CRB objective in P. As written, no primal block is driven by the sensing term. In Alg. 1 / P1.1 (Eq. 45) only the rate is maximized with {U,F} fixed, so the CRB contribution is constant and R_x is chosen purely for secrecy (plus AN). P1.2 (Eq. 59) then only recovers a feasible U for that already-fixed R_x (PDD merely enforces F=Θ_r G R_x G^H Θ_r^H). In P2 (Eq. 62) U is again held fixed, rendering tr(U^{-1}) constant; the explicit Wirtinger gradients (64)–(67) contain solely the μ-weighted rate terms and omit the (1−μ) derivative of tr(U^{-1}) through F(Θ_r). Consequently the iterates cannot realize a μ-dependent tradeoff. Yet Fig. 8 shows clear CRB variation with μ, which is impossible under the stated updates. Either the gradients/algorithm description are incomplete (CRB cha
  2. [§III-B (final paragraph); problem P] End of §III-B states that eavesdropper DOAs θ are treated as known and fixed (taken from the previous coherent block) inside the optimization. All CRB constraints and the equivalent channel H_e = h_e^H Θ_r G therefore assume perfect, static geometry. The paper never quantifies degradation under DOA mismatch or intra-block motion, nor does it close the loop with an actual estimator. Because both the sensing guarantee and the secrecy metric depend on the same θ, this is a load-bearing modeling assumption; at minimum a sensitivity study (or an alternating estimate-and-optimize experiment) is required to support the claims in Figs. 3–8.
  3. [§IV-B1, Eqs. (49)–(57)] Rank-one recovery after SDR is asserted by citing Theorem 1 of [38] (downlink beamforming with SINR constraints). The secrecy-rate objective after SCA is a difference of logs involving both legitimate and eavesdropper channels, plus a free sensing covariance R_s; it is not identical to the SINR-constrained multicast/unicast setting of [38]. A short argument (or numerical rank statistics of the returned W_n) is needed to confirm that the reconstructed w_n^* remain optimal for (45), otherwise the “global optimum” claim for P1.1 is unsupported.
minor comments (6)
  1. [§IV] Section title IV reads “SECRECY RATE AND SECRCECY RATE TRADEOFF” (duplication/typo).
  2. [throughout] Multiple typos and grammar issues: “secrcecy”, “he CRB”, “exapmle”, “achieveable”, “intergation”, “mainpluate”, “anntena”, “verus”, “differnet”, “transmsion”, “leaken”, “optimizating”, “martix”, etc. A thorough proof-read is needed.
  3. [§III-B, §IV] Notation drift: β vs α for the complex target gain; ˜α vs ˜β in the FIM blocks; Z_s vs Y_s for the echo; q_s/q_c introduced without definition until the simulation section; constraint numbering jumps (61a after 62).
  4. [Eqs. (3), (68); Alg. 3] The orthogonal-phase constraint cos(ϕ_t,k − ϕ_r,k)=0 (Eq. 3) is stated for lossless STAR-RIS but never enforced in the PGM projection (68), which only normalizes amplitudes. Clarify whether independent phases are assumed or the constraint is dropped.
  5. [§V, Table I] Simulation parameters (Table I) list Rayleigh fading while the model (7)–(9) is Rician; reconcile. Also state how q_s=1, q_c=1e-6 are obtained from the “maximum secrecy rate / minimum CRB” normalization.
  6. [§V] Figs. 3–4 baselines (“Transmit Optimization Only”, “STAR-RIS Optimization Only”) are not fully specified (which variables frozen, same μ?). Add one sentence each.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: standard ISAC optimization formulation whose objective, CRB derivation, and BCD blocks are defined independently of the reported simulation outcomes.

full rationale

This is a conventional engineering design paper. The secrecy-rate expression (22), the FIM/CRB derivation (33)–(40), and the weighted tradeoff problem P (41) are written from first principles (SINR definitions, Gaussian observation model, Schur-complement CRB). The hybrid BCD split (P1.1 SCA+SDR, P1.2 PDD, P2 PGM) is an algorithmic procedure applied to that fixed problem; normalization constants qs, qc and weight μ are free design knobs, not parameters fitted to force a claimed constant. Simulation figures compare the algorithm against transmit-only and STAR-RIS-only baselines under the same model—they are numerical outcomes, not “predictions” that reduce to fitted inputs by construction. Self-citations to related RIS/ISAC work by overlapping authors appear only as background motivation and do not supply a uniqueness theorem or ansatz that closes the central claim. Any defect in whether the written Wirtinger gradients actually couple the CRB term into every block is a correctness/implementation issue, not circularity. Hence score 0 with empty steps.

Assumptions & free parameters 4 free parameters · 7 assumptions · 0 invented entities

The claim rests on standard array-signal-processing and wireless-comms modeling choices plus several paper-specific idealizations (blocked direct links, fixed known DOA, ES STAR-RIS with orthogonal phase, advanced eavesdropper that decodes the aggregate stream). No new physical entities. Free parameters are the usual design knobs (μ, q_s, q_c, power, array sizes) and algorithmic step sizes, not data-fitted constants that manufacture the result.

free parameters (4)
  • tradeoff weight μ = 0.5 (default)
    Hand-chosen scalar balancing secrecy rate vs. CRB in objective (41); default 0.5, swept in Fig. 8. Directly controls the reported tradeoff surface.
  • normalization constants q_s, q_c = q_s=1, q_c=1e-6
    Set by hand to q_s=1, q_c=1e-6 so the two objective terms are numerically comparable; not derived from first principles.
  • PGM step sizes μ_t, μ_r and PDD penalty schedule (ρ, m, η)
    Algorithmic hyperparameters required for convergence of Algorithms 2–3; values not fully specified beyond qualitative description.
  • Rician factor ε and path-loss coefficients α_i
    Channel model parameters; numerical values not fully tabulated beyond geometry and noise power, yet they shape all SINR/CRB numbers.
assumptions (7)
  • domain assumption Direct BS–target and target–sensor links are fully blocked; only STAR-RIS reflection path illuminates and observes the target.
    Stated in §II; without it the sensing signal model (17) and H_e change and the STAR-RIS necessity weakens.
  • domain assumption STAR-RIS elements obey energy splitting β_t,k² + β_r,k² = 1 and lossless orthogonal phase cos(φ_t,k − φ_r,k) = 0.
    Eqs. (2)–(3), citing [35]; ideal passive lossless model.
  • ad hoc to paper Eavesdropper DOAs are known and fixed during each optimization window (from prior coherent block).
    End of §III-B; load-bearing for writing CRB as a deterministic function of W, R_s, Θ_r.
  • domain assumption Eavesdropper jointly decodes the superposition of all users’ messages; secrecy rate = sum user rates − single aggregate eavesdropping rate.
    §III-A and [37]; defines R_s in (22).
  • domain assumption Channels static over L samples; R_x ≈ (1/L)XX^H is accurate for large L; perfect CSI at the designer.
    §II and (5)–(6); standard ISAC coherent-block assumption, never relaxed in the paper.
  • domain assumption SDR rank-one recovery for the multiuser beamforming subproblem always yields a global optimum of the original nonconvex problem via the construction in [38].
    §IV-B1 after (56); invoked without re-proof for this secrecy-rate objective.
  • standard math Fisher information / CRB formulas for Gaussian observations and Wirtinger gradients for complex phase optimization are valid.
    Used throughout §III-B and §IV-B2.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Beamforming and Phase Shift Design for STAR-RIS Assisted Secure Sensing and Communication in ISAC Systems." pith.science (2026). https://pith.science/paper/BMVYR3MV

@misc{pith2026260728081,
  author       = {Pith},
  title        = {Pith review of: Beamforming and Phase Shift Design for STAR-RIS Assisted Secure Sensing and Communication in ISAC Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BMVYR3MV}},
  note         = {Machine review of arXiv:2607.28081}
}
read the original abstract

Integrated sensing and communication(ISAC), as a rapidly advancing technique, introduces a fresh approach for achieving secure communication and intelligent sensing for future wireless networks. An ISAC framework empowered by simultaneously transmitting and reflecting reconfigurable intelligent surfaces(STAR-RIS) is explored in this paper, where a base station equipped with multiple antennas establishes wireless links to users each with a single antenna during the detection of a point target. The point target, regarded as an eavesdropper, trying to intercept users' information. Cramer-Rao bound(CRB) serves as evaluation criterion to assess sensing accuracy of point eavesdropper, whereas the secrecy rate is employed to quantify the security level of the communication link. To optimize sensing-communication tradeoff, a joint optimization problem is constructed. To approach the formulated problem, a hybrid Block Coordinate Descent(BCD)-based algorithm is developed, which alternately updates the transmission beamforming and STAR-RIS phase shifts, using successive convex approximation(SCA) technique, penalty dual decomposition (PDD) framework and projected gradient method(PGM).

Figures

Figures reproduced from arXiv: 2607.28081 by the authors.

Figure 1
Figure 1. An ISAC system empowered by STAR-RIS matrices, respectively, which is expressed as 𝚯𝑟 = diag(𝛽𝑟,1𝑒 𝑗 𝜙𝑟,1 , . . . , 𝛽𝑟,𝐾 𝑒 𝑗 𝜙𝑟,𝐾 ), (1a) 𝚯𝑡 = diag(𝛽𝑡,1𝑒 𝑗 𝜙𝑡,1 , . . . , 𝛽𝑡,𝐾 𝑒 𝑗 𝜙𝑡,𝐾 ), (1b) where 𝛽𝑖,𝑘 ∈ [0, 1] and 𝜙𝑖,𝑘 ∈ [0, 2𝜋], ∀𝑖 ∈ {𝑡, 𝑟} represent the amplitude and phase shifts of element 𝑘. In the model where phase shifts of transmission and reflec￾tion are treated independently, it is postulated that the ph… view at source ↗
Figure 2
Figure 2. Convergence behavior of algorithm transmit beamforming subproblem is reformulated as a SDP problem, solved by the interior-point method within the PDD framework. Given a solution accuracy 𝜀, the per-iteration complexity is calculated as O [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. CRB versus BS antennas with two benchmarks 8 12 16 20 24 Number of BS Antennas (M) -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Tradeoff Value Proposed Algorithm Transmit Optimization Only STAR-RIS Optimization Only [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Tradeoff Value versus BS antennas with two benchmarks shifts are updated via a projected gradient method, allowing each block to be efficiently optimized while fixing the others. As expected, the converged objective value exhibits a positive correlation with the transm…
Figure 5
Figure 5. Figure 5: CRB versus different BS antennas 8 12 16 20 24 Number of BS Antennas (M) 0.8 1 1.2 1.4 1.6 1.8 2 Rs P = 20dBm P = 25dBm P = 30dBm [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Secrecy Rate versus different BS antennas 8 12 16 20 24 Number of Sensors (S) 0 0.2 0.4 0.6 0.8 1 1.2 1.4 CRB 10-6 P = 25dBm P = 30dBm P = 35dBm [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: CRB versus different sensors 28 29 30 31 32 Transmit Power (dBm) 0 0.5 1 1.5 2 2.5 3 CRB 10-7 mu = 0.5 mu = 0.7 mu = 0.9 [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

41 extracted references · 1 linked inside Pith

  1. [38]

    Joint transmit beamforming for multiuser MIMO communications and MIMO radar,

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

    2020

  2. [1]

    Joint Radar Sensing, Location, and Communication Resources Optimization in 6G Network,

    H. Zhang, B. Chen, X. Liu and C. Ren, “Joint Radar Sensing, Location, and Communication Resources Optimization in 6G Network,” IEEE J. Sel. Areas Commun. , vol. 42, no. 9, pp. 2369-2379, Sept. 2024

    2024

  3. [2]

    MU-MIMO Commu- nications with MIMO Radar: From Co-existence to Joint Transmission,

    F. Liu, C. Masouros, A. Li, H. Sun, and L. Hanzo, “MU-MIMO Commu- nications with MIMO Radar: From Co-existence to Joint Transmission,” IEEE Trans. Wireless Commun. , vol. 17, no. 4, pp. 2755–2770, Apr. 2018

    2018

  4. [3]

    Integrating Sensing and Communi- cations for Ubiquitous IoT: Applications, Trends, and Challenges,

    Y . Cui, F. Liu, X. Jing, and J. Mu, “Integrating Sensing and Communi- cations for Ubiquitous IoT: Applications, Trends, and Challenges,” IEEE Netw., vol. 35, no. 5, pp. 158–167, Sep./Oct. 2021

    2021

  5. [4]

    A Tutorial on Joint Radar and Communication Transmission for Vehicular Networks—Part I: Background and Funda- mentals,

    F. Liu and C. Masouros, “A Tutorial on Joint Radar and Communication Transmission for Vehicular Networks—Part I: Background and Funda- mentals,” IEEE Commun. Lett. , vol. 25, no. 2, pp. 322–326, Feb. 2021

    2021

  6. [5]

    Toward Multi- functional 6G Wireless Networks: Integrating Sensing, Communication, and Security,

    Z. Wei, F. Liu, C. Masouros, N. Su, and A. Petropulu, “Toward Multi- functional 6G Wireless Networks: Integrating Sensing, Communication, and Security,” IEEE Commun. Mag. , vol. 60, no. 4, pp. 65–71, Apr. 2022

    2022

  7. [6]

    Integrated Sensing and Communication for 6G Holographic Digital T wins,

    H. Zhang, Z. Zhang, X. Liu, W. Li, H. Li and C. Sun, “Integrated Sensing and Communication for 6G Holographic Digital T wins,” IEEE Wireless Commun., 2025

    2025

  8. [7]

    Integrated Sensing and Communications: Toward Dual- functional Wireless Networks for 6G and Beyond,

    F. Liu et al., “Integrated Sensing and Communications: Toward Dual- functional Wireless Networks for 6G and Beyond,” IEEE J. Sel. Areas Commun., vol. 40, no. 6, pp. 1728–1767, Jun. 2022

    2022

Show all 41 references
  1. [8]

    Sensing for Secure Communication in ISAC: Protocol Design and Beamforming Optimization,

    Y . Cao, L. Duan and R. Zhang, “Sensing for Secure Communication in ISAC: Protocol Design and Beamforming Optimization,” IEEE Trans. Wireless Commun. , vol. 24, no. 2, pp. 1207-1220, Feb. 2025

    2025

  2. [9]

    Beamforming in Integrated Sensing and Communication Systems With Reconfigurable Intelligent Surfaces,

    R. S. P . Sankar, S. P . Chepuri and Y . C. Eldar, “Beamforming in Integrated Sensing and Communication Systems With Reconfigurable Intelligent Surfaces,” IEEE Trans. Wireless Commun. , vol. 23, no. 5, pp. 4017-4031, May 2024

    2024

  3. [10]

    Intelligent Reflect- ing Surface Enabled Sensing: Cramér–Rao Lower Bound Optimization,

    X. Song, J. Xu, F. Liu, T. X. Han, and Y . C. Eldar, “Intelligent Reflect- ing Surface Enabled Sensing: Cramér–Rao Lower Bound Optimization,” IEEE Trans. Signal Process. , vol. 71, pp. 2011–2026, 2023

    2011

  4. [11]

    Integrated Sensing and Communication with Reconfigurable Intelligent Surfaces: Opportunities, Applications, and Future Directions,

    R. Liu, M. Li, H. Luo, Q. Liu and A. L. Swindlehurst, “Integrated Sensing and Communication with Reconfigurable Intelligent Surfaces: Opportunities, Applications, and Future Directions,” IEEE Wireless Com- mun., vol. 30, no. 1, pp. 50-57, Feb. 2023

    2023

  5. [12]

    Capacity Maximization in RIS-UA V Networks: A DDQN-Based Tra- jectory and Phase Shift Optimization Approach,

    H. Zhang, M. Huang, H. Zhou, X. Wang, N. Wang and K. Long, “Capacity Maximization in RIS-UA V Networks: A DDQN-Based Tra- jectory and Phase Shift Optimization Approach,” IEEE Trans. Wireless Commun., vol. 22, no. 4, pp. 2583-2591, Apr. 2023

    2023

  6. [13]

    Mobile Edge Intel- ligence and Computing With Star-RIS Assisted Intelligent Autonomous Transport System,

    Z. Wu, H. Zhang, L. Li, Y . Lu, C. Sun and H. Li, “Mobile Edge Intel- ligence and Computing With Star-RIS Assisted Intelligent Autonomous Transport System,” IEEE Trans. Intell. Transp. Syst. , pp. 1-12, 2025

    2025

  7. [14]

    Joint Waveform De- sign and Passive Beamforming for RIS-assisted Dual-Functional Radar- Communication System,

    X. Wang, Z. Fei, Z. Zheng, and J. Guo, “Joint Waveform De- sign and Passive Beamforming for RIS-assisted Dual-Functional Radar- Communication System,” IEEE Trans. Veh. Technol. , vol. 70, no. 5, pp. 5131–5136, May 2021

    2021

  8. [15]

    Joint Beamforming Design for RIS-Assisted Integrated Sensing and Communication Systems,

    H. Luo, R. Liu, M. Li, Y . Liu, and Q. Liu, “Joint Beamforming Design for RIS-Assisted Integrated Sensing and Communication Systems,” IEEE Trans. Veh. Technol., vol. 71, no. 12, pp. 13393–13397, Dec. 2022

    2022

  9. [16]

    Cramér-Rao Bound Minimization for IRS-Enabled Multiuser Integrated Sensing and Communications,

    X. Song, X. Qin, J. Xu and R. Zhang, “Cramér-Rao Bound Minimization for IRS-Enabled Multiuser Integrated Sensing and Communications,” IEEE Trans. Wireless Commun. , vol. 23, no. 8, pp. 9714-9729, Aug. 2024

    2024

  10. [17]

    Optimized Design for IRS- Assisted Integrated Sensing and Communication Systems in Clutter Environments,

    C. Liao, F. Wang, and V . K. N. Lau, “Optimized Design for IRS- Assisted Integrated Sensing and Communication Systems in Clutter Environments,” IEEE Trans. Commun. , vol. 71, no. 8, pp. 4721–4734, Aug. 2023

    2023

  11. [18]

    STARS Enabled Integrated Sensing and Communications,

    Z. Wang, X. Mu, and Y . Liu, “STARS Enabled Integrated Sensing and Communications,” IEEE Trans. Wireless Commun. , vol. 22, no. 10, pp. 6750–6765, Oct. 2023

    2023

  12. [19]

    Joint Active and Passive Beamforming Design for Reconfigurable Intelligent Surface Enabled Integrated Sensing and Communication,

    Z. Xing, R. Wang, and X. Yuan, “Joint Active and Passive Beamforming Design for Reconfigurable Intelligent Surface Enabled Integrated Sensing and Communication,” IEEE Trans. Commun. , vol. 71, no. 4, pp. 2457– 2474, Apr. 2023

    2023

  13. [20]

    Joint Active and Passive Beamforming Design for IRS-Aided Radar-Communication,

    M. Hua, Q. Wu, C. He, S. Ma, and W. Chen, “Joint Active and Passive Beamforming Design for IRS-Aided Radar-Communication,” IEEE Trans. Wireless Commun. , vol. 22, no. 4, pp. 2278–2294, Apr. 2023

    2023

  14. [21]

    A Reconfigurable Intel- ligent Surface Aided Dual-Function Radar and Communication System,

    S. Y an, S. Cai, W. Xia, J. Zhang, and S. Xia, “A Reconfigurable Intel- ligent Surface Aided Dual-Function Radar and Communication System,” Proc. 2nd IEEE Int. Symp. Joint Commun. Sens. , Seefeld, Austria, Mar. 2022, pp. 1–6

    2022

  15. [22]

    Joint Transmit Waveform and Passive Beamforming Design for RIS-Aided DFRC Systems,

    R. Liu, M. Li, Y . Liu, Q. Wu, and Q. Liu, “Joint Transmit Waveform and Passive Beamforming Design for RIS-Aided DFRC Systems,” IEEE J. Sel. Topics Signal Process. , vol. 16, no. 5, pp. 995–1010, Aug. 2022

    2022

  16. [23]

    Intelligent Reflecting Surface Enabled Sensing: Cramér–Rao Bound Optimization,

    X. Song, J. Xu, F. Liu, T. X. Han, and Y . C. Eldar, “Intelligent Reflecting Surface Enabled Sensing: Cramér–Rao Bound Optimization,” IEEE Trans. Signal Process. , vol. 71, pp. 2011–2026, May 2023

    2011

  17. [24]

    Joint Waveform and Discrete Phase Shift Design for RIS-Assisted Integrated Sensing and Communi- cation System under Cramér–Rao Bound Constraint,

    X. Wang, Z. Fei, J. Huang, and H. Yu, “Joint Waveform and Discrete Phase Shift Design for RIS-Assisted Integrated Sensing and Communi- cation System under Cramér–Rao Bound Constraint,” IEEE Trans. Veh. Technol., vol. 71, no. 1, pp. 1004–1009, Jan. 2022

    2022

  18. [25]

    Physical Layer Security in Wireless Networks: A Tutorial,

    Y . -S. Shiu, S. Y . Chang, H. -C. Wu, S. C. . -H. Huang and H. -H. Chen, “Physical Layer Security in Wireless Networks: A Tutorial,” IEEE Wireless Commun., vol. 18, no. 2, pp. 66-74, Apr. 2011

    2011

  19. [26]

    Secrecy Rate Optimizations for MIMO Communication Radar,

    A. Deligiannis, A. Daniyan, S. Lambotharan and J. A. Chambers, “Secrecy Rate Optimizations for MIMO Communication Radar,” IEEE Trans. Aerosp. Electron. Syst. , vol. 54, no. 5, pp. 2481-2492, Oct. 2018

    2018

  20. [27]

    DRL-Based Secrecy Rate Optimization for RIS-Assisted Secure ISAC Systems,

    Q. Liu, Y . Zhu, M. Li, R. Liu, Y . Liu and Z. Lu, “DRL-Based Secrecy Rate Optimization for RIS-Assisted Secure ISAC Systems,” IEEE Trans. Veh. Technol., vol. 72, no. 12, pp. 16871-16875, Dec. 2023

    2023

  21. [28]

    Secure Beamforming for RIS-Aided ISAC System With CRB Minimization,

    S. Y ang, C. Qi, W. Ci and W. Luo, “Secure Beamforming for RIS-Aided ISAC System With CRB Minimization,” IEEE Commun. Lett. , vol. 29, no. 6, pp. 1390-1394, Jun. 2025

    2025

  22. [29]

    Joint Beamforming and Reflection Design for Secure RIS-ISAC Systems,

    J. Chu, Z. Lu, R. Liu, M. Li and Q. Liu, “Joint Beamforming and Reflection Design for Secure RIS-ISAC Systems,” IEEE Trans. Veh. Technol., vol. 73, no. 3, pp. 4471-4475, Mar. 2024

    2024

  23. [30]

    Secure Transmission for IRS-Aided UA V-ISAC Networks,

    J. Zhang, J. Xu, W. Lu, N. Zhao, X. Wang and D. Niyato, “Secure Transmission for IRS-Aided UA V-ISAC Networks,”IEEE Trans. Wireless Commun., vol. 23, no. 9, pp. 12256-12269, Sept. 2024

    2024

  24. [31]

    Joint Waveform and Beamforming Design for RIS-Aided ISAC Systems,

    K. Zhong, J. Hu, C. Pan, M. Deng and J. Fang, “Joint Waveform and Beamforming Design for RIS-Aided ISAC Systems,” IEEE Signal Processing Lett. , vol. 30, pp. 165-169, 2023

    2023

  25. [32]

    STAR: Simultaneous Transmission and Reflection for 360° Coverage by Intelligent Surfaces,

    Y . Liu et al., “STAR: Simultaneous Transmission and Reflection for 360° Coverage by Intelligent Surfaces,” IEEE Wireless Commun. , vol. 28, no. 6, pp. 102-109, Dec. 2021

    2021

  26. [33]

    Proxi- mal Policy Optimization-Based Transmit Beamforming and Phase-Shift Design in an IRS-Aided ISAC System for the THz Band,

    X. Liu, H. Zhang, K. Long, M. Zhou, Y . Li and H. V . Poor, “Proxi- mal Policy Optimization-Based Transmit Beamforming and Phase-Shift Design in an IRS-Aided ISAC System for the THz Band,” IEEE J. Sel. Areas Commun. , vol. 40, no. 7, pp. 2056-2069, Jul. 2022

  27. [34]

    Stacked Intelligent Metasurfaces and STAR-RIS-enabled Terahertz ISAC System,

    K. Ebrahimi, Z. Mehrzad, S. Javadi, M. R. Mili, E. Jorswieck and N. Al-Dhahir, “Stacked Intelligent Metasurfaces and STAR-RIS-enabled Terahertz ISAC System,” IEEE Trans. Veh. Technol. , pp. 1-6, 2025

    2025

  28. [35]

    J. Xu, Y . Liu, X. Mu, R. Schober and H. V . Poor, ”STAR-RISs: A Cor- related T&R Phase-Shift Model and Practical Phase-Shift Configuration Strategies,” IEEE J. Sel. Topics in Signal Process. , vol. 16, no. 5, pp. 1097-1111, Aug. 2022

    2022

  29. [36]

    Penalty Dual Decomposition Method for Non- smooth Nonconvex Optimization—Part I: Algorithms and Convergence Analysis,

    Q. Shi and M. Hong, “Penalty Dual Decomposition Method for Non- smooth Nonconvex Optimization—Part I: Algorithms and Convergence Analysis,” IEEE Trans. Signal Process. , vol. 68, pp. 4108–4122, Jun. 2020

    2020

  30. [37]

    Wei et al., ”Cramér-Rao Bound and Secure Transmission Trade-Off Design for Semi-IRS-Enabled ISAC,” IEEE Trans

    W . Wei et al., ”Cramér-Rao Bound and Secure Transmission Trade-Off Design for Semi-IRS-Enabled ISAC,” IEEE Trans. Wireless Commun. , vol. 23, no. 11, pp. 15753-15767, Nov. 2024

    2024

  31. [39]

    A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization,

    M. Razaviyayn, M. Hong, and Z.-Q. Luo, “A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization,” SIAM J. Optim. , vol. 23, no. 2, pp. 1126–1153, 2013

    2013

  32. [40]

    Rotatable Antenna Enabled Wireless Communication: Modeling and Optimization,

    B. Zheng, Q. Wu, T. Ma, and R. Zhang, “Rotatable Antenna Enabled Wireless Communication: Modeling and Optimization,” arXiv preprint arXiv:2501.02595, 2025

    2025

  33. [41]

    Rotatable Antenna Enabled Wireless Communication and sensing: Opportunities and Challenges,

    B. Zheng, T. Ma, C. Y ou, J. Tang, R. Schober, and R. Zhang, “Rotatable Antenna Enabled Wireless Communication and sensing: Opportunities and Challenges,” arXiv preprint arXiv:2505.16828 , 2025. 14 Haijun Zhang (Fellow, IEEE) is currently a Full Professor at University of Scie...

    2025 arXiv

Pith tools

Reviewed July 31, 2026 · model on record in the stance chip above.