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REVIEW 5 major objections 5 minor 43 references

Backscatter Device-aided Integrated Sensing and Communication: A Pareto Optimization Framework

T0 review · 5 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Passive backscatter devices can push an integrated sensing and communication system's Pareto frontier about 15% higher while costing 50–80% less than a reconfigurable intelligent surface.

desk verdict A genuinely new system concept—ambient BDs as relays for ISAC—undermined by a physical model that assumes coherent controllable BD phases while the paper's own premises say BDs are asynchronous and uncontrolled, plus several load-bearing derivation errors. read the letter →

arxiv 2507.09354 v1 pith:QPDBAS5I submitted 2025-07-12 cs.CR

Yifan Zhang , Yu Bai , Riku Jantti , Zheng Yan , Christos Masouros , Zhu Han This is my paper
classification cs.CR
keywords integratedsensingandcommunicationbackscatterdeviceOFDMParetoboundarymutualinformationresourceallocationblockcoordinatedescentreconfigurableintelligentsurface
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

Integrated sensing and communication (ISAC) lets one base station send data and sense its surroundings on the same OFDM waveform, but dense obstacles weaken both jobs. This paper proposes using cheap, passive backscatter devices already present in the environment as additional reflectors, creating extra signal paths for both the user and the sensing receiver. It then defines the Pareto boundary — the set of best achievable trade-offs — between sensing mutual information and communication rate, and solves the joint problem of choosing which subcarriers serve sensing versus data, how much power each gets, and what binary phase each backscatter device applies. The claimed result is a trade-off frontier about 15% above conventional ISAC in both metrics, with hardware cost 50–80% below a reconfigurable-intelligent-surface benchmark (programmable reflecting arrays) and less than a 5% performance penalty. If this holds, 6G networks gain a low-cost way to restore ISAC performance in obstructed, non-line-of-sight settings.

What carries the argument

The central object is the Pareto boundary of the achievable performance region: the curve of (sensing mutual information, communication rate) pairs at which neither metric can rise without the other falling. The metrics themselves are built from two effective channels — $H_c = H_{b,u} + \sum_k b_{k,m} H_{b,k} H_{k,u}$ for the user and $G = H_{s,t} + \sum_k b_{k,m} H_{s,k} H_{k,t}$ for sensing — in which each backscatter device's binary phase factor $b_{k,m} = \alpha_k e^{j\phi_{k,m}}$ ($\phi_{k,m}\in\{0,\pi\}$) enters as a coherent channel term. The frontier is computed by block coordinate descent over three subproblems: resource-element allocation relaxed through an $\ell_0$-norm approximation solved by successive convex approximation, power allocation reduced to a closed-form water-filling update by an augmented Lagrangian, and backscatter phases lifted into a semidefinite matrix $X = xx^H$ and rounded from its dominant eigenvector. Compactness and normality of the region guarantee that sweeping the rate constraint in one problem and the sensing constraint in the other covers the whole boundary.

What would settle it

Re-run the Pareto optimization with each backscatter device's binary phase replaced by an independent random variable per OFDM symbol, or measured from unsynchronized commercial tags; if the sensing-versus-rate frontier moves back toward the no-backscatter curve, the claimed coherent gain is an artifact of the model.

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Extended reading notes

Core claim

The paper's central claim is that a backscatter-device-assisted ISAC system, in which ordinary ambient backscatter devices reflect the base station's OFDM signal toward the user and back from the target, attains a near-optimal Pareto boundary between sensing mutual information (SMI) and communication rate, and that this boundary sits clearly above the no-backscatter baseline. The achievable (SMI, rate) region is shown to be compact and normal, which guarantees the full Pareto frontier can be traced by two constrained problems: maximize SMI subject to a minimum rate, and maximize rate subject to a minimum SMI. A block coordinate descent algorithm solves these by alternating subcarrier assignment (successive convex approximation), power allocation (an augmented-Lagrangian water-filling step), and discrete backscatter phase selection (semidefinite relaxation). Simulations report roughly 15% gains in both SMI and rate over a conventional ISAC system without backscatter devices, and a hardware cost 50–80% below a reconfigurable-intelligent-surface benchmark with less than a 5% performance penalty. The same algorithmic structure is argued to extend to bistatic sensing (separate transmit and receive sites) and to MIMO systems.

Load-bearing premise

The 15% gain depends on the user and sensing receiver knowing each backscatter device's reflection phase and adding the direct and reflected signals coherently, even though the paper's own model says the devices reflect at unsynchronized, locally controlled time instants.

Editorial extensions

If this is right

  • A designer can read the required operating point directly from the frontier: for any minimum communication rate it gives the maximum sensing mutual information and the subcarrier, power, and phase settings that achieve it.
  • Deploying more backscatter devices, or placing them closer to the base station, target, or user, shifts the frontier outward; the simulations show gains growing with device count and shrinking with device distance.
  • The gap between the fully optimized scheme and the scheme with fixed backscatter phases shows that even uncontrolled ambient devices already help, but phase control buys a visible additional edge.
  • Because the same block coordinate descent structure survives bistatic and MIMO generalizations, the Pareto framework is not limited to the same-site single-antenna setup simulated in the main results.

Reading between the lines

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

  • Beyond the paper, the reported 15% gain should be read as an upper envelope: the model assumes receivers know each backscatter device's phase state and combine reflections coherently, while the paper itself notes these devices switch at unsynchronized instants; recomputing the frontier with random per-symbol phase offsets is a direct stress test of that assumption.
  • The phase-optimization step could be repurposed as a robustness tool: if some backscatter devices are malicious or malfunctioning, the same semidefinite-relaxation machinery can quantify how much the frontier degrades or be used to detect devices whose reflected phases do not match the optimized pattern.
  • The cost comparison omits calibration and installation labor for the backscatter devices themselves; if each device needs per-unit calibration, the true saving lies between the 50% figure for the phase-controlled scheme and the 80% figure for the uncontrolled scheme.
  • In dense multipath, extra reflected paths may help through diversity rather than coherent phase addition; comparing the frontier for random-but-fixed device phases against the optimized-phase frontier would separate those two mechanisms.
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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. This paper studies a BD-assisted monostatic OFDM ISAC system. It formulates two Pareto-boundary search problems that maximize sensing mutual information (SMI) subject to a communication-rate constraint and vice versa, with binary RE allocation, per-RE power, and binary BD phase-shift modulation as optimization variables. The authors propose a BCD algorithm: SCA with an ℓ0-norm approximation for RE allocation, an augmented-Lagrangian water-filling method for power, and SDR plus thresholding for BD phases. Simulations report roughly 15% gains in SMI and rate over a BD-free benchmark and 50–80% cost savings over a RIS benchmark. Extensions to bistatic and MIMO settings are discussed.

Significance. The Pareto framework is a useful way to present the sensing-communication trade-off, and the problem decomposition into RE/power/BD-phase subproblems is structurally reasonable. The paper is explicit about computational complexity and includes a cost model. However, the headline numerical claims rest on a physically questionable coherent-combining model and on an invalid relaxation step, and the scenario labels are internally contradictory. If the model is corrected and the simulations rerun, the qualitative conclusion that optimized BDs can improve ISAC performance may survive, but the specific 15% and 50–80% numbers are not currently supported. The paper does not provide code or full simulation details (e.g., number of channel realizations), which limits reproducibility.

major comments (5)
  1. [Section II.B; Eqs. (5), (7)–(9); Algorithm 1 (P5)] The BD phase optimization in subproblem (P5) and the 'Proposed, SPP' simulation require the BS to know and control each BD's phase state and for the reflected contributions to add coherently after OFDM demodulation. This is contradicted by Section II.B, which states that 'BDs operate asynchronously, reflecting signals at discrete, unsynchronized time instants governed by each device's local control or energy-harvesting schedules, rather than centralized phase alignment.' For an asynchronous tag the phase offset and switching instant are not known or stable, so the complex sum in (5)–(9) and the optimized {+1,-1} pattern are not physically implementable. Please either restrict the system model to synchronized, phase-controllable BDs and state this assumption explicitly, or rework the receive model to include random phase offsets and noncoherent combining; the current manuscript cannot support both the asynchronous premise and the coherent phase optimization.
  2. [Section II.B, Remark 3, Section V.A] The scenario labels are inconsistent. Section II.B defines Scenario 1 as fixed (predetermined) modulation and Scenario 2 as dynamic control, and Remark 3 states that Scenario 1 omits subproblem (P5) while Scenario 2 includes it. However, Section V.A labels 'Proposed, SPP' (with phase optimization) as Scenario 1 and 'Proposed, SP' (without phase optimization) as Scenario 2, the reverse of Remark 3. This makes it unclear which physical scenario is simulated and undermines the comparison in Fig. 6.
  3. [Eqs. (20)–(28), Section IV.B.1] Equation (22) is not a relaxation of the binary constraint. For the function g(w;δ) in (21), g is strictly concave on [0,1] with g(0)=0 and g(1)=1, hence g(w) ≥ w for all w∈[0,1]. The constraint g(I)−I ≤ 0 therefore forces g(I)=I, i.e., I∈{0,1}; it reproduces the integer constraint exactly instead of relaxing it. The subsequent first-order Taylor replacement (23)–(26) turns the constraint into a different, stricter linear cut (because the tangent of a concave function is an upper bound), and the 0.5-thresholding in (28) is an undocumented heuristic. The authors should either provide a genuine continuous relaxation (e.g., the convex hull of {0,1} or an exact penalty formulation) and justify the thresholding, or explicitly prove that the linearized surrogate preserves the optimal binary solution.
  4. [Eq. (8), Section II.C.2] The sensing receive model Yr = α_t G G^H X with G = Hs,t + Σ_k b_k Hs,k Hk,t is scalar, so GG^H = |G|^2. This is not the usual monostatic round-trip backscatter model, which would involve the product of the forward and return channels (e.g., a term proportional to G^2 or a separate return-path channel) and would apply the BD modulation to each traversal, producing b_k^2 on the BD round-trip path. The current form cancels the phase of the total one-way channel and changes how the BD phases enter the SMI. Please derive (8) from a two-way channel model or clarify the assumptions under which |G|^2 is the correct echo gain.
  5. [Section V.B, cost model] The cost-savings percentages do not follow from the stated formulas. With c=2, Cost_BD,SPP=(1/2+50/23)NC0 ≈ 2.67 NC0 versus Cost_RIS=(100/23)NC0 ≈ 4.35 NC0, giving a saving of about 39%, not 'approximately 50%'. For the SP scheme, Cost_BD,SP=(7/23)NC0 gives a saving of about 93%, not 80%. Please correct either the formulas or the claimed percentages and re-plot Figs. 10–11 if needed.
minor comments (5)
  1. [Section V.A and Fig. 6] The benchmark is called 'Ref. [33]' in the text but labeled 'Ref. [32]' in the figure; the reference list contains both [32] and [33], so please align them.
  2. [Eq. (31)] The penalty update uses ∥∇C∥_k / ∥∇C∥_{k−1}, which is undefined if the previous gradient norm is zero; a safeguard should be added.
  3. [Algorithm 1, line 4] The convergence check ∥N^{(s+1)}_{r,m}−N^{(s−1)}_{r,m}∥_0 uses the ℓ0 norm on a set; please clarify the intended vector notation.
  4. [Eq. (34)] The water-filling expressions contain an ambiguous placement of the factor ln2·Δf; please rewrite the formula to make the numerator and bracket structure unambiguous.
  5. [Section V.A] The simulation section does not state how many independent channel realizations are averaged, nor the SDR randomization sample count; adding these details would improve reproducibility.

Circularity Check

1 steps flagged · score 4.0 of 10

Pareto algorithmics are self-contained, but the headline ~15% BD gain is directionally entailed by Eqs. (7)-(9) and the phase-optimization premise conflicts with the paper's own asynchronous, no-precise-control BD assumption.

  1. self definitional [Section II.C, Eqs. (7)-(9); Section IV.B.3, subproblem (P5); Section V.B, benchmark comparison]
    "the data communication rate is then given as Cd = ... log2( 1 + |Hb,u + PK k=1 bk,mHb,kHk,u|^2 / σ2c × E{|Xc(m, n)|^2} ) ... Specifically, it is approximately 15% improvements in both the SMI and the data rate between "Proposed, SPP" and "Ref. [32]"."

    Eqs. (7) and (9) define SMI and rate with BD reflections as additive coherent channel terms inside the SNR (bk,m ∈ {±1}); subproblem (P5) maximizes exactly those |H|^2 forms. The uniform average over the ±1 hypercube gives E_b[|Hb,u + Σ bk Hb,kHk,u|^2] = |Hb,u|^2 + Σ|Hb,kHk,u|^2 ≥ |Hb,u|^2, so the optimizer is guaranteed to meet or exceed the BD-free SNR on every RE. The direction of the headline "approximately 15% improvements... compared to conventional ISAC systems without leveraging BDs" is therefore entailed by the definitions plus the optimization, not an independent prediction; Section V.B's "thereby validating the effectiveness and superiority of the proposed algorithm" presents a construction as a finding.

full rationale

The analytical derivation chain is predominantly self-contained. The Pareto-boundary machinery (Definitions 1-3, Lemmas III.1-III.2) is established by the paper's own proofs in Appendices A-B via standard compactness/normality and scalarization arguments over a finite power/subcarrier budget; the gradient formulas (35)-(36) are derived in Appendix C; and subproblems are solved in-paper (ℓ0-SCA at (21)-(26), water-filling at (34), SDR at (43)-(45)). External works [24], [25], [34], [35] anchor only the standard Pareto/BCD framing, while self-citations [11], [19], [23] are incidental modeling citations (fixed-modulation scenario, prior BD-ISAC framing, BD-aided MIMO channels) and carry no load-bearing weight; no uniqueness theorem is invoked. The headline gain is a partial exception and is flagged as one circular step: the SMI and rate in Eqs. (7) and (9) are defined with BD reflections as additive coherent gains, and subproblem (P5) maximizes exactly those quantities, so the direction of the reported "~15% improvement" over BD-free ISAC is guaranteed by the construction; only the magnitude is simulated. Correctness flaws (not circularity) that further weaken the gain claim: Section II.B says BDs "operate asynchronously... rather than centralized phase alignment" and Section I.B rules out "precise control," yet the SPP gain requires per-symbol phase control and BS knowledge of BD cascade channels; Section V.A labels SPP/SP as Scenario 1/2, inverted relative to Remark 3; the cost formula with c=2 gives 38.5% (SPP) and 93% (SP) savings, not the stated 50% and 80%; and Section V.B cites "Ref. [32]" for the 15% comparison while Section V.A names Ref. [33] as the no-BD benchmark. These are evidence/consistency problems, not circular reductions, but they compound the by-construction direction of the central performance claim.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The central contribution rests on six unverified hand-set parameters (alpha_k, cost ratio c, delta, beta_t, Pmax, ALM initializations) plus five modeling axioms (coherent BD combination, round-trip phase cancellation, epsilon-constraint completeness for the nonconvex region, ideal full-duplex cancellation, and the OFDM channel model). Of these, the coherent-combination axiom and the phase-cancellation axiom are the most load-bearing: if real asynchronous tags do not combine coherently or if the round-trip phase does not cancel, the simulated gains in Figs. 6-8 are upper bounds rather than achievable performance. No new physical entities are postulated; the BDs are existing off-the-shelf devices and the 'additional reflection paths' are instances of the standard multipath model, so the invented-entities ledger is empty.

free parameters (6)
  • Backscatter attenuation coefficient alpha_k = 0.5 (Table I)
    Set by hand; multiplies every BD reflection term in Eqs. (5)-(9), so the magnitude of the claimed sensing and rate gains scales with this choice.
  • Cost ratio c between a metasurface element and a simple antenna = 2
    Set by hand in Section V.B ('we set c=2'); the claimed 50-80% cost reduction versus RIS is computed directly from this ratio and the ad hoc 23%-element cost share.
  • Smoothing constant delta in the ell0 approximation = unspecified
    Needed in Eqs. (21)-(24) to define the RE relaxation; no value is reported, so the implemented relaxation cannot be reproduced.
  • Annealing slope beta_t in the BD phase mapping = unspecified
    Controls the tanh annealing in Eq. (45) that maps the SDR solution to binary phases; the schedule is never specified.
  • Per-RE power cap Pmax = unspecified
    Appears in constraint (10d), which is solved in every subproblem, but is absent from Table I.
  • ALM multipliers lambda, rho and tolerances epsilon_1-epsilon_3 = unspecified
    Algorithm 1 requires these, including the update rule (30)-(31); no initialization or schedule is given.
assumptions (5)
  • domain assumption The BD and BS channels combine coherently in (5)-(9) with known phases
    Section II.C writes the received signal as the sum of direct and BD-reflected components with explicit phases bk,m; this presumes the receiver knows and can track each BD path, while Section II.B says BDs are asynchronous.
  • domain assumption Round-trip sensing channel factorizes as alpha_t G G^H with BD phase canceling
    In Eq. (8) the Hermitian product cancels the BD modulation phase (bk bk^* = |bk|^2); a passive BD reflecting both forward and return would apply the phase twice (bk^2).
  • standard math The epsilon-constraint method enumerates the complete Pareto boundary of a nonconvex region
    Lemma III.2 and Appendix B rely on compactness and normality; for nonconvex regions the constraint method can return weakly Pareto points, and with an approximate solver the recovered curve is an inner bound of the true boundary.
  • domain assumption Full-duplex self-interference at the BS is perfectly canceled and radar/communication subcarriers share one power budget
    Stated in Section II.C.2 and constraint (10c); residual self-interference or separate power rails would shift the SMI formula and the Pareto boundary.
  • standard math OFDM multipath model (6) with Rayleigh fading and Gaussian RCS alpha_t describes BD and target propagation
    All SMI and rate expressions inherit this channel model; it is standard for OFDM ISAC analysis.

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Cite this review

Pith. "Pith review of Backscatter Device-aided Integrated Sensing and Communication: A Pareto Optimization Framework." pith.science (2026). https://pith.science/paper/QPDBAS5I

@misc{pith2026250709354,
  author       = {Pith},
  title        = {Pith review of: Backscatter Device-aided Integrated Sensing and Communication: A Pareto Optimization Framework},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QPDBAS5I}},
  note         = {Machine review of arXiv:2507.09354}
}
read the original abstract

Integrated sensing and communication (ISAC) systems potentially encounter significant performance degradation in densely obstructed urban and non-line-of-sight scenarios, thus limiting their effectiveness in practical deployments. To deal with these challenges, this paper proposes a backscatter device (BD)-assisted ISAC system, which leverages passive BDs naturally distributed in underlying environments for performance enhancement. These ambient devices can enhance sensing accuracy and communication reliability by providing additional reflective signal paths. In this system, we define the Pareto boundary characterizing the trade-off between sensing mutual information (SMI) and communication rates to provide fundamental insights for its design. To derive the boundary, we formulate a performance optimization problem within an orthogonal frequency division multiplexing (OFDM) framework, by jointly optimizing time-frequency resource element (RE) allocation, transmit power management, and BD modulation decisions. To tackle the non-convexity of the problem, we decompose it into three subproblems, solved iteratively through a block coordinate descent (BCD) algorithm. Specifically, the RE subproblem is addressed using the successive convex approximation (SCA) method, the power subproblem is solved using an augmented Lagrangian combined water-filling method, and the BD modulation subproblem is tackled using semidefinite relaxation (SDR) methods. Additionally, we demonstrate the generality of the proposed system by showing its adaptability to bistatic ISAC scenarios and MIMO settings. Finally, extensive simulation results validate the effectiveness of the proposed system and its superior performance compared to existing state-of-the-art ISAC schemes.

Figures

Figures reproduced from arXiv: 2507.09354 by the authors.

Figure 1
Figure 1. Applications of backscatter devices in the real world. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. System model of a BD-assisted ISAC system. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Convergence behaviors of the proposed algorithm. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (3 more)
Figure 5
Figure 5. Figure 5: Pareto boundaries for different power budgets. 0 3 6 9 12 15 Communication Rate (bps/Hz) 0 5 10 15 20 25 30 35 SMI (bps/Hz) RIS-based Proposed, SPP Proposed, SP Ref. [32] TDMA/FDMA [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 8
Figure 8. Figure 8: Sensing and communication performance under various BD activity [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: Cost distribution of a RIS. 32 33 34 35 36 Maximum SMI (bps/Hz) 40 120 200 280 360 440 Cost RIS-based Proposed,SPP Proposed, SP [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]

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Works this paper leans on

43 extracted references · 40 canonical work pages

  1. [24]

    Cooperative ISAC with direct localization and rate-splitting multiple access communication: A Pareto optimization framework,

    P. Gao, L. Lian, and J. Yu, “Cooperative ISAC with direct localization and rate-splitting multiple access communication: A Pareto optimization framework,” IEEE JSAC, vol. 41, no. 5, pp. 1496–1515, May 2023

    work page 2023

  2. [25]

    Unified ISAC Pareto boundary based on mutual information and minimum mean- square error estimation,

    S. Wang, L. Chen, J. Zhou, Y . Chen, K. Han, and C. You, “Unified ISAC Pareto boundary based on mutual information and minimum mean- square error estimation,” IEEE TWC , vol. 72, no. 11, pp. 6783–6795, May 2024

    work page 2024

  3. [34]

    Joint radar-communication transmission: A generalized pareto optimization framework,

    L. Chen, F. Liu, W. Wang, and C. Masouros, “Joint radar-communication transmission: A generalized pareto optimization framework,” IEEE Transactions on Signal Processing, vol. 69, pp. 2752–2765, May. 2021

    work page 2021

  4. [1]

    Integrated sensing and communication signals toward 5G-A and 6G: A survey,

    Z. Wei, H. Qu, Y . Wang, X. Yuan, H. Wu, Y . Du, K. Han, N. Zhang, and Z. Feng, “Integrated sensing and communication signals toward 5G-A and 6G: A survey,” IEEE IoTJ, vol. 10, no. 13, pp. 11 068–11 092, Jan. 2023

    work page 2023

  5. [2]

    Enabling intelligent connectivity: A survey of secure ISAC in 6G networks,

    X. Zhu, J. Liu, L. Lu, T. Zhang, T. Qiu, C. Wang, and Y . Liu, “Enabling intelligent connectivity: A survey of secure ISAC in 6G networks,”IEEE Communications Surveys & Tutorials , vol. 27, no. 2, pp. 748–781, Jul. 2024

    work page 2024

  6. [3]

    A survey on fundamental limits of integrated sensing and communication,

    A. Liu, Z. Huang, M. Li, Y . Wan, W. Li, T. X. Han, C. Liu, R. Du, D. K. P. Tan, J. Lu, Y . Shen, F. Colone, and K. Chetty, “A survey on fundamental limits of integrated sensing and communication,” IEEE Communications Surveys & Tutorials, vol. 24, no. 2, pp. 994–1034, Feb. 2022

    work page 2022

  7. [4]

    Integrated sensing and commu- nication for wireless extended reality (xr) with reconfigurable intelligent surface,

    T. Ma, Y . Xiao, X. Lei, and M. Xiao, “Integrated sensing and commu- nication for wireless extended reality (xr) with reconfigurable intelligent surface,” IEEE Journal of Selected Topics in Signal Processing , vol. 17, no. 5, pp. 980–994, Aug. 2023

    work page 2023

  8. [5]

    Integrated sensing and communications: Toward dual-functional wire- less 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: Toward dual-functional wire- less networks for 6G and beyond,” IEEE JSAC, vol. 40, no. 6, pp. 1728– 1767, Jun. 2022

    work page 2022

Show all 43 references
  1. [6]

    SNR/CRB-constrained joint beamforming and reflection designs for RIS-ISAC systems,

    R. Liu, M. Li, Q. Liu, and A. L. Swindlehurst, “SNR/CRB-constrained joint beamforming and reflection designs for RIS-ISAC systems,” IEEE TWC, vol. 23, no. 7, pp. 7456–7470, Jul. 2023

    2023

  2. [7]

    NOMA-assisted full space STAR- RIS-ISAC,

    N. Xue, X. Mu, Y . Liu, and Y . Chen, “NOMA-assisted full space STAR- RIS-ISAC,” IEEE TWC, vol. 23, no. 8, pp. 8954–8968, Jan. 2024

    2024

  3. [8]

    Cram ´er-Rao bound optimization for active ris-empowered isac systems,

    Q. Zhu, M. Li, R. Liu, and Q. Liu, “Cram ´er-Rao bound optimization for active ris-empowered isac systems,” IEEE TWC, vol. 23, no. 9, pp. 11 723–11 736, Apr. 2024

    2024

  4. [9]

    Joint wave- form and beamforming design in RIS-ISAC systems: A model-driven learning approach,

    P. Jiang, M. Li, R. Liu, W. Wang, and Q. Liu, “Joint wave- form and beamforming design in RIS-ISAC systems: A model-driven learning approach,” IEEE Trans. Commun. , 2025, early Access, doi: 10.1109/TCOMM.2025.3535869

    2025

  5. [10]

    RIS-assisted integrated sensing and communications: A subspace rotation approach,

    X. Meng, F. Liu, S. Lu, S. P. Chepuri, and C. Masouros, “RIS-assisted integrated sensing and communications: A subspace rotation approach,” in IEEE Radar Conference, San Antonio , May 2023

    2023

  6. [11]

    On the achievable rate of bistatic modulated rescatter systems,

    R. Duan, R. J ¨antti, H. Yi˘gitler, and K. Ruttik, “On the achievable rate of bistatic modulated rescatter systems,” IEEE Transactions on Vehicular Technology, vol. 66, no. 10, pp. 9609–9613, Jun. 2017

    2017

  7. [12]

    Ambient backscatter communications: A contemporary survey,

    N. Van Huynh, D. T. Hoang, X. Lu, D. Niyato, P. Wang, and D. I. Kim, “Ambient backscatter communications: A contemporary survey,” IEEE Communications Surveys & Tutorials , vol. 20, no. 4, pp. 2889–2922, May 2018

    2018

  8. [13]

    Recent advances in passive UHF- RFID tag antenna design for improved read range in product packaging applications: A comprehensive review,

    R. Abdulghafor, S. Turaev, H. Almohamedh, R. Alabdan, B. Almutairi, A. Almutairi, and S. Almotairi, “Recent advances in passive UHF- RFID tag antenna design for improved read range in product packaging applications: A comprehensive review,” IEEE Access, vol. 9, Apr. 2021

    2021

  9. [14]

    Backscatter communication meets practical battery-free internet of things: A survey and outlook,

    T. Jiang, Y . Zhang, W. Ma, M. Peng, Y . Peng, M. Feng, and G. Liu, “Backscatter communication meets practical battery-free internet of things: A survey and outlook,” IEEE Communications Surveys & Tu- torials, vol. 25, no. 3, pp. 2021–2051, May. 2023

    2021

  10. [15]

    NOMA-based backscatter communications: Fundamentals, applications, and advancements,

    M. Ahmed, M. Shahwar, F. Khan, W. Ullah Khan, A. Ihsan, U. Sadiq Khan, F. Xu, and S. Chatzinotas, “NOMA-based backscatter communications: Fundamentals, applications, and advancements,” IEEE IoTJ, vol. 11, no. 11, pp. 19 303–19 327, Apr. 2024

    2024

  11. [16]

    Maximizing the value of service provisioning in multi-user ISAC systems through fairness guaranteed collaborative resource allocation,

    B. Li, X. Wang, and F. Fang, “Maximizing the value of service provisioning in multi-user ISAC systems through fairness guaranteed collaborative resource allocation,” IEEE JSAC, vol. 42, no. 9, pp. 2243– 2258, Jun. 2024

    2024

  12. [17]

    Toward full passive internet of things: Symbiotic lo- calization and ambient backscatter communication,

    C. Ren and L. Liu, “Toward full passive internet of things: Symbiotic lo- calization and ambient backscatter communication,” IEEE IoTJ, vol. 10, no. 22, pp. 19 495–19 506, Mar. 2023

    2023

  13. [18]

    Joint localization and signal detection for ambient backscatter communication systems,

    X. Xu, T. Huang, X. Kuai, and Y .-C. Liang, “Joint localization and signal detection for ambient backscatter communication systems,” IEEE TWC, vol. 23, no. 10, pp. 14 437–14 451, Jun. 2024

    2024

  14. [19]

    Net-zero integrated sensing and communication in backscatter systems,

    Y . Zhang, T. Xu, C. Masouros, and Z. Han, “Net-zero integrated sensing and communication in backscatter systems,” in IEEE GLOBECOM, Cape Town, South Africa , May 2024, pp. 1894–1899

    2024

  15. [20]

    Integrated sensing and backscatter communication,

    D. Galappaththige, C. Tellambura, and A. Maaref, “Integrated sensing and backscatter communication,” IEEE WCL, vol. 12, no. 12, pp. 2043– 2047, Aug. 2023

    2023

  16. [21]

    ISAC with backscattering RFID tags: Joint beamforming design,

    H. Luo, U. Demirhan, and A. Alkhateeb, “ISAC with backscattering RFID tags: Joint beamforming design,” in IEEE ICC, Denver, 2024, pp. 921–925

    2024

  17. [22]

    B-ISAC: Backscatter integrated sensing and communication for IoE applications,

    Z. Zhao, Y . Dong, T. Wei, X. Tang, X.-P. Zhang, and Z. Liu, “B-ISAC: Backscatter integrated sensing and communication for IoE applications,” arXiv preprint arXiv:2407.19235 , Jul. 2024

    2024 arXiv

  18. [23]

    Artificial rich scattering- assisted mimo systems using passive backscatter devices,

    A. Al-Nahari, R. J ¨antti, and M. U. Sheikh, “Artificial rich scattering- assisted mimo systems using passive backscatter devices,” in 3rd Infor- mation Communication Technologies Conference (ICTC), NanJing, May, 2022, pp. 19–24

    2022

  19. [26]

    Cross- domain dual-functional OFDM waveform design for accurate sens- ing/positioning,

    F. Zhang, T. Mao, R. Liu, Z. Han, S. Chen, and Z. Wang, “Cross- domain dual-functional OFDM waveform design for accurate sens- ing/positioning,” IEEE JSAC, vol. 42, no. 9, pp. 2259–2274, Jun. 2024

    2024

  20. [27]

    Tse and P

    D. Tse and P. Viswanath, Fundamentals of Wireless Communication . Cambridge, U.K.: Cambridge University Press, 2005

    2005

  21. [28]

    5G PRS-based sensing: A sensing reference signal approach for joint sensing and communication system,

    Z. Wei, Y . Wang, L. Ma, S. Yang, Z. Feng, C. Pan, Q. Zhang, Y . Wang, H. Wu, and P. Zhang, “5G PRS-based sensing: A sensing reference signal approach for joint sensing and communication system,” IEEE TVT, vol. 72, no. 3, pp. 3250–3263, Oct. 2022

    2022

  22. [29]

    IoT communica- tions with M-PSK modulated ambient backscatter: Algorithm, analysis, and implementation,

    J. Qian, A. N. Parks, J. R. Smith, F. Gao, and S. Jin, “IoT communica- tions with M-PSK modulated ambient backscatter: Algorithm, analysis, and implementation,” IEEE IoTJ, vol. 6, no. 1, pp. 844–855, Jul. 2018

    2018

  23. [30]

    Energy-efficient UA V backscatter communication with joint trajectory design and resource optimization,

    G. Yang, R. Dai, and Y .-C. Liang, “Energy-efficient UA V backscatter communication with joint trajectory design and resource optimization,” IEEE TWC, vol. 20, no. 2, pp. 926–941, Feb. 2021

    2021

  24. [31]

    Full-duplex OFDM radar with LTE and 5G NR waveforms: Challenges, solutions, and mea- surements,

    C. Baquero Barneto, T. Riihonen, M. Turunen, L. Anttila, M. Fleischer, K. Stadius, J. Ryyn ¨anen, and M. Valkama, “Full-duplex OFDM radar with LTE and 5G NR waveforms: Challenges, solutions, and mea- surements,” IEEE Transactions on Microwave Theory and Techniques , vol. 67, n...

    2019

  25. [32]

    Optimized waveforms for 5G–6G communication with sensing: Theory, simulations and experiments,

    S. D. Liyanaarachchi, T. Riihonen, C. B. Barneto, and M. Valkama, “Optimized waveforms for 5G–6G communication with sensing: Theory, simulations and experiments,” IEEE TWC , vol. 20, no. 12, pp. 8301– 8315, Jun. 2021

    2021

  26. [33]

    Multicarrier radar-communications wave- form design for RF convergence and coexistence,

    M. Bic ˘a and V . Koivunen, “Multicarrier radar-communications wave- form design for RF convergence and coexistence,” in IEEE ICASSP , Brighton, U.K., May 2019, pp. 7780–7784

    2019

  27. [35]

    Convergence of a block coordinate descent method for nondifferentiable minimization,

    P. Tseng, “Convergence of a block coordinate descent method for nondifferentiable minimization,” Journal of Optimization Theory and Applications, vol. 109, pp. 475–494, Jun. 2001

    2001

  28. [36]

    Clutter suppression, time-frequency synchronization, and sensing parameter as- sociation in asynchronous perceptive vehicular networks,

    X. Wang, S. Yang, J. Zhang, C. Masouros, and P. Zhang, “Clutter suppression, time-frequency synchronization, and sensing parameter as- sociation in asynchronous perceptive vehicular networks,” IEEE Journal on Selected Areas in Communications , vol. 42, no. 10, pp. 2719–2736, Aug. 2024

    2024

  29. [37]

    Modulation in the air: Backscatter communication over ambient OFDM carrier,

    G. Yang, Y . C. Liang, R. Zhang, and Y . Pei, “Modulation in the air: Backscatter communication over ambient OFDM carrier,” IEEE IEEE Trans. Commun., vol. 66, no. 3, pp. 1219–1233, Nov. 2017

    2017

  30. [38]

    Integration of visible light and backscatter communications for ambient internet of things,

    B. Xie, A. Dowhuszko, K. Koskinen, L. Mela, J. Lietz ´en, K. Ruttik, R. J¨antti, and J. H¨am¨al¨ainen, “Integration of visible light and backscatter communications for ambient internet of things,” in IEEE VTC-Spring , Singapore, Jun. 2024

    2024

  31. [39]

    Location sensing and beamforming design for irs-enabled multi-user ISAC systems,

    Z. Yu, X. Hu, C. Liu, M. Peng, and C. Zhong, “Location sensing and beamforming design for irs-enabled multi-user ISAC systems,” IEEE Transactions on Signal Processing, vol. 70, pp. 5178–5193, Nov. 2022

    2022

  32. [40]

    Full-duplex communication for ISAC: Joint beamforming and power optimization,

    Z. He, W. Xu, H. Shen, D. W. K. Ng, Y . C. Eldar, and X. You, “Full-duplex communication for ISAC: Joint beamforming and power optimization,” IEEE JASC, vol. 41, no. 9, pp. 2920–2936, Jun. 2023

    2023

  33. [41]

    Model reconfigurable intelligent surfaces with cdl channels,

    MathWorks, “Model reconfigurable intelligent surfaces with cdl channels,” https://www.mathworks.com/help/5g/ug/model- reconfigurable-intelligent-surfaces-with-cdl-channels.html, 2024

    2024

  34. [42]

    Reconfig- urable holographic surface-enabled multi-user wireless communications: Amplitude-controlled holographic beamforming,

    R. Deng, B. Di, H. Zhang, Y . Tan, and L. Song, “Reconfig- urable holographic surface-enabled multi-user wireless communications: Amplitude-controlled holographic beamforming,” IEEE TWC , vol. 21, no. 8, pp. 6003–6017, Jan. 2022

    2022

  35. [43]

    Data-driven analysis of the cost-performance trade-off of reconfigurable intelligent surfaces in a production network,

    M. Rossanese, A. Garcia-Saavedra, A. E. Lutu, and X. Costa Perez, “Data-driven analysis of the cost-performance trade-off of reconfigurable intelligent surfaces in a production network,” Proceedings of the ACM on Networking, vol. 1, no. CoNEXT3, pp. 1–20, Nov. 2023

    2023

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