Pith. sign in

REVIEW 4 major objections 4 minor 43 references

Drift-Adaptive Slicing-Based Resource Management for Cooperative ISAC Networks

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read The paper claims that a digital-twin-based, drift-adaptive slicing scheme can keep cooperative ISAC network planning accurate when device and target distributions shift, delivering up to 18% higher service satisfaction and up to 13.1%…

desk verdict Credible extension of the authors' ICCC'24 drift-adaptive DT work to ISAC slicing, with sound closed-form planning and a real ensemble idea; the unquantified drift-detection threshold and self-referential emulation keep the headline gains from being fully supported yet. read the letter →

arxiv 2506.20762 v1 pith:2WII7YK3 submitted 2025-06-25 cs.NI eess.SP

Shisheng Hu , Jie Gao , Xue Qin , Conghao Zhou , Xinyu Huang , Mushu Li , Mingcheng He , Xuemin Shen This is my paper · ORCID
classification cs.NIeess.SP
keywords integratedsensingandcommunicationnetworkslicingdigitaltwinmodeldriftspatialpointprocessemulationresourcemanagementcooperativeISAC
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

Cooperative integrated sensing and communication (ISAC) networks must decide in advance how much spectrum and computing to reserve for communication and sensing services, but the spatial distributions of mobile devices and targets drift over time, so a spatial model that fits one planning window can mislead the next. This paper tries to establish that a drift-adaptive scheme based on digital twins of the two network slices can keep those planning decisions reliable without abandoning closed-form computation. Each slice twin pairs a statistical spatial model with an emulation function: the model layer switches between a joint cluster process and an independent Poisson process depending on detected drift, and the emulation layer replays historical snapshots to choose between the candidate decisions. The claimed payoff is a planning method that raises the actual service satisfaction ratio by up to 18% and lowers overall resource consumption by up to 13.1% relative to benchmarks, while keeping the optimization essentially one-dimensional and closed-form.

What carries the argument

The load-bearing machinery is the slice-level digital twin, defined as a digital representation of a network slice that collects location data, adapts a statistical spatial model, and emulates candidate decisions. Its two components do the work: (i) a drift-adaptive spatial modeling function that toggles between a Thomas cluster process (joint modeling) and two independent Poisson point processes (independent modeling), with drift detected by mean absolute percentage error in parameter prediction and adapted by LSTM retraining plus model ensemble; and (ii) a network emulation function that reconstructs network instances from historical snapshots and scores a planning decision by the average relative difference between service demand and capacity. Together they reduce the original multi-variable optimization to a closed-form solution in the decision variables plus a one-dimensional search over the communication active probability $\rho_c$.

What would settle it

Re-run the 400-window experiment with drift events more frequent than the LSTM retraining lag (for example, flipping the thinning probability $\nu$ every few planning windows instead of every 200) and with abrupt sign changes in device-target correlation; if the proposed scheme's satisfaction ratio falls to or below the plain independent-model benchmark while its drift indicator stays low, the temporal-correlation premise is falsified.

Watch

Extended reading notes

Core claim

The central discovery is that model drift in ISAC network planning can be handled by maintaining digital twins of the sensing and communication slices, where each twin combines a drift-adaptive statistical model with a network emulation function. The statistical modeling is an ensemble of two spatial point processes: a joint Thomas cluster process that captures the attraction between mobile devices and targets (more accurate when the pattern is stable), and an independent homogeneous Poisson process for each population (less accurate but robust when correlation changes abruptly). Drift is detected by monitoring the prediction errors of LSTM-based parameter forecasts, and when drift is flagged the twin retrains the predictors and outputs both models; the emulation function then reconstructs instances from historical spatial snapshots, measures the demand-capacity gap of the planning decision produced by each model, and selects the better one. On the paper's account this yields closed-form planning decisions, with only the communication active probability requiring a one-dimensional search, and delivers up to 18% higher service satisfaction and up to 13.1% lower resource consumption than the benchmark schemes.

Load-bearing premise

The emulation function assumes historical spatial snapshots are representative of the upcoming planning window, so the method selects a model by replaying the past; if the device and target distribution shifts discontinuously between windows, the replayed evaluation can pick the wrong model and satisfaction drops.

Editorial extensions

If this is right

  • Network planning for cooperative ISAC can be computed in near-closed form despite non-stationary spatial distributions, so the scheme scales to many APs without a combinatorial optimizer.
  • When device-target spatial correlation is volatile, deliberately using a less detailed but more robust spatial model can outperform a more detailed one; characterizing correlation is not always beneficial.
  • After an abrupt drift, the proposed model-update mechanism brings the joint model's prediction error back down quickly, and the ensemble selector prevents both under-provisioning and over-provisioning in the intervening windows.
  • The reported gains (up to 18% satisfaction, up to 13.1% resource reduction) grow with drift frequency, meaning the scheme's advantage is largest exactly in the non-stationary regimes where fixed models fail.

Reading between the lines

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

  • The same emulation-plus-ensemble recipe could be applied beyond spatial point processes, e.g., to choose between a fine-grained and a coarse model of user traffic demand or mobility, since the selection mechanism only needs historical snapshots and a demand-capacity metric.
  • Because the emulation function trusts recent history, the method's advantage should shrink as drift events become more frequent than the LSTM training horizon; a stress test with drift every few planning windows would reveal the failure boundary.
  • The closed-form structure suggests the planning decision could be recomputed at a finer timescale after drift is detected, shortening the vulnerable window rather than waiting for the next planning window; the paper does not explore this adaptive-horizon extension.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper proposes a drift-adaptive, slicing-based resource management scheme for cooperative ISAC networks. A global controller establishes separate sensing and communication slices and, over large timescales, makes planning decisions including sensing RoI partitioning, the active probability for communication, and spectrum/edge-computing reservations. To handle non-stationary spatial distributions, the paper builds slice digital twins containing two statistical spatial models (a Thomas cluster process and an independent Poisson point process) with LSTM-based parameter prediction, a drift detection/adaptation module, and a network emulation function that evaluates candidate decisions. The planning problem is transformed via closed-form optimality conditions (Propositions 1–3) into a one-dimensional search. Numerical results claim an up-to-18% improvement in service satisfaction and up-to-13.1% reduction in resource consumption against benchmark schemes.

Significance. The paper's main contribution is a closed-form, computationally light planning solution for a complex resource allocation problem in non-stationary cooperative ISAC networks, together with a model-ensemble mechanism that is intended to be robust to spatial model drift. The derivations in Propositions 1–3 are internally coherent, and the reduction to a single-variable search is a genuine strength. However, the central quantitative claims rest on an unquantified drift-detection threshold and on an emulation step that partially reuses the model's own rate formula, so the reported 18% and 13.1% numbers are not yet reproducible or fully independent. If these gaps are closed, the framework would be a useful step toward practically implementable network planning in dynamic environments.

major comments (4)
  1. [Section IV-B3] The model drift detection rule is specified only qualitatively as 'drastic accuracy degradation' in MAPE. No threshold, statistical test, or detection rule is given, and no sensitivity analysis is provided. Since this detection triggers the LSTM retraining and the ensemble switch, the reported gains of up to 18% satisfaction and 13.1% resource reduction are all downstream of this unspecified quantity. The paper must specify the detection criterion (e.g., MAPE > θ) and show how the results vary with θ; otherwise the algorithm is under-specified and the numerical claims cannot be reproduced or assessed for robustness.
  2. [Section V-B1] The communication-side emulation function reuses Eq. (9), which is the same model-derived rate lower bound used in the planning decision. This creates a circularity: the decision evaluator validates a model partly using that model's own formula. Only the sensing-side evaluation uses actual device and target locations. The paper should evaluate communication capacity by an independent mechanism (e.g., direct simulation of the transmission process) or explicitly acknowledge that the communication emulation is not an independent model check.
  3. [Section VI] The simulation results report averages over only 5 runs with no error bars, confidence intervals, or statistical significance tests. Given the stochastic nature of point processes and LSTM training, the observed differences (e.g., 1.6%, 2.9%, 13.1%) could be within run-to-run noise. The authors should provide variance/confidence information and, ideally, a sensitivity analysis of the drift-detection threshold that is currently missing.
  4. [Section IV-C] The emulation function assumes that historical spatial snapshots are representative of the upcoming planning window. This temporal-correlation assumption is load-bearing, but Fig. 9(b) shows that satisfaction dips in the windows right after an abrupt drift, indicating that the emulation can select a poorer decision during transitions. The paper should quantify this transient degradation and discuss how the scheme behaves under discontinuous distribution shifts.
minor comments (4)
  1. [Section IV-B3] There is a typo: 'exhibit exhibits' should read 'exhibit.'
  2. [Section VI-B] The text says 'the standard derivation of the distance' where 'standard deviation' is intended.
  3. [Section IV-A] The data collection period M0 is set to 10 in Table I but no rationale is given for this choice; a brief justification would improve reproducibility.
  4. [Section V-C] The overall algorithm is described only in prose; a pseudocode listing would make the decision flow (detection, update, ensemble selection) unambiguous.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: closed-form planning formulas are derived from stated stochastic-geometry models, and drift-adaptive ensemble selection is validated on independent sensing-location counts; reuse of Eq. (9) in communication evaluation is a stated simplification, not a circular derivation.

full rationale

The paper does not exhibit a circular derivation chain. The planning formulas are obtained from stated system models: Eq. (9) follows from Proposition 1 via Jensen's inequality on a PPP/Poisson thinning model, Eqs. (7) combine the sensing constraints, and Eq. (10) follows from an external interference CDF result [23]. The LSTM parameter predictors are fitted to MLE reference values from past windows and are then checked against emulation instances, so the output planning decision is not a re-statement of the fitted values. The drift detector evaluates MAPE of parameter prediction errors, and the ensemble selector chooses between decisions based on emulation instances that, on the sensing side, count actual device and target locations rather than reusing either spatial model. The communication-side evaluation does reuse Eq. (9), but the paper states this explicitly as a complexity reduction, applies the same formula to every candidate decision, and ties the drift-adaptation gain to the sensing-side counts and parameter-update behavior rather than to the communication formula alone, so this is a validation simplification rather than a result that reduces to its own input by construction. Self-citations ([1], [9], [15], [26], [27]) are contextual and none is load-bearing. The unspecified MAPE threshold for drift detection is a reproducibility and sensitivity gap, not a circularity.

Assumptions & free parameters 2 free parameters · 5 assumptions · 1 invented entities

The central planning formulas rest on two spatial point process models, a temporal-correlation assumption for emulation, and several approximations, plus an unquantified drift threshold and unspecified LSTM setup. These are the main things a reader must accept before the headline numbers transfer to a real deployment.

free parameters (2)
  • Drift detection MAPE threshold = not specified
    In Section IV-B.3, H^S_k and H^D_k are set when parameter prediction exhibits drastic accuracy degradation, but no numerical threshold or statistical test is given; the detector is effectively tuned to make the simulation work.
  • LSTM lookback K0 and hyperparameters = not specified
    LSTM networks predict point process intensities and cluster parameters throughout Section IV-B, but K0, architecture, training epochs, and learning rate are not reported, making the drift adaptation non-reproducible.
assumptions (5)
  • domain assumption Spatial distributions can be represented as either a Thomas cluster process or two independent PPPs, with parameters constant within a planning window and LSTM-predictable across windows.
    Section IV-B assumes these point process models; all planning formulas derive from them, and real industrial distributions may not fit either model well.
  • domain assumption Historical spatial snapshots are temporally correlated with the upcoming planning window, so emulation on past instances validates future decisions.
    Stated in the introduction and Section IV-C. The method selects between decisions based on past instances; this can fail when distributions change discontinuously, as the simulation itself shows in windows 201 to 204.
  • domain assumption Only the strongest interfering mobile device matters for SIR, beam directions are independent and uniformly distributed, and interferers form a thinned PPP.
    Used in Appendix B to derive D^I,U_max via [23, Prop. 1]. This ignores cumulative interference from multiple devices in the same sensing band.
  • domain assumption E[N^U_I,1] equals lambda^U / lambda^I for independent modeling and equals mu^U for joint modeling.
    These approximations appear in Section V-A.2 and are load-bearing for sensing demand and capacity formulas. The joint-model value ignores cluster overlap and target reassignment to non-parent devices.
  • domain assumption Mobile devices and APs are homogeneous, so one representative device and AP characterize the whole network.
    Stated in Section III-C and used to derive the rate, sensing, and interference formulas. Heterogeneous deployments would need clustering or additional modeling.
invented entities (1)
  • Network slice digital twins (slice DTs)
    purpose: Digital representations of sensing and communication slices for spatial modeling, drift adaptation, and decision emulation.
    The paper provides no external validation or falsifiable handle for the DTs; their utility is demonstrated only inside the synthetic simulation.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Drift-Adaptive Slicing-Based Resource Management for Cooperative ISAC Networks." pith.science (2026). https://pith.science/paper/2WII7YK3

@misc{pith2026250620762,
  author       = {Pith},
  title        = {Pith review of: Drift-Adaptive Slicing-Based Resource Management for Cooperative ISAC Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2WII7YK3}},
  note         = {Machine review of arXiv:2506.20762}
}
read the original abstract

In this paper, we propose a novel drift-adaptive slicing-based resource management scheme for cooperative integrated sensing and communication (ISAC) networks. Particularly, we establish two network slices to provide sensing and communication services, respectively. In the large-timescale planning for the slices, we partition the sensing region of interest (RoI) of each mobile device and reserve network resources accordingly, facilitating low-complexity distance-based sensing target assignment in small timescales. To cope with the non-stationary spatial distributions of mobile devices and sensing targets, which can result in the drift in modeling the distributions and ineffective planning decisions, we construct digital twins (DTs) of the slices. In each DT, a drift-adaptive statistical model and an emulation function are developed for the spatial distributions in the corresponding slice, which facilitates closed-form decision-making and efficient validation of a planning decision, respectively. Numerical results show that the proposed drift-adaptive slicing-based resource management scheme can increase the service satisfaction ratio by up to 18% and reduce resource consumption by up to 13.1% when compared with benchmark schemes.

Figures

Figures reproduced from arXiv: 2506.20762 by the authors.

Figure 1
Figure 1. The considered scenario. drift before their actual implementation. III. SYSTEM MODEL AND PROBLEM FORMULATION A. Considered Scenario As shown in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. The considered beam pattern of mobile devices and an [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. The DT of the sensing slice. computing resources, respectively; constraint (8e) ensures that the expected transmission rate of each mobile device is greater than a threshold Rˆ; constraint (8f) ensures that the expected number of targets that can be tracked with the requirements on the sensing data quantity and processing time satisfied is greater than the expected number of the targets in all the RoIs in the covera… view at source ↗
Figures from the paper (6 more)
Figure 5
Figure 5. Figure 5: Values of the parameters µ U k and σ U k in the joint spatial modeling across planning windows. 0 100 200 300 400 Planning Window Index 1 2 3 4 Parameter Value 10-5 Reference Prediction (a) λ I k 0 100 200 300 400 Planning Window Index 0.01 0.02 0.03 0.04 Parameter Val…
Figure 6
Figure 6. Figure 6: Values of the parameters λ I k and λ U k in the independent spatial modeling across planning windows. follows a Poisson distribution with mean µ U k . At the end of the k-th planning window, using the spatial distribution data collected in this planning window, the ref…
Figure 8
Figure 8. Figure 8: Modeling error across planning windows. Unlike in the ideal cases where the joint modeling outperforms the independent modeling, in the non-ideal cases, it can be observed from [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]
Figure 7
Figure 7. Figure 7: Performance evaluation with varying number of mobil [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 9
Figure 9. Figure 9: Performance evaluation in the early planning window [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]
Figure 10
Figure 10. Figure 10: Performance evaluation under varying model drift fr [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

43 extracted references · 41 canonical work pages

  1. [1]

    Model d rift- adaptive resource reservation in ISAC networks: A digital t win-based approach,

    S. Hu, J. Gao, X. Huang, C. Zhou, M. He, and X. Shen, “Model d rift- adaptive resource reservation in ISAC networks: A digital t win-based approach,” in Proc. IEEE/CIC Int. Conf. Commun. China (ICCC) , 2024, pp. 2143–2148

    work page 2024

  2. [2]

    Integrated sensing and communications: Toward dual-func tional wire- less networks for 6G and beyond,

    F. Liu, Y . Cui, C. Masouros, J. Xu, T. X. Han, Y . C. Eldar, an d S. Buzzi, “Integrated sensing and communications: Toward dual-func tional wire- less networks for 6G and beyond,” IEEE J. Sel. Areas Commun. , vol. 40, no. 6, pp. 1728–1767, 2022

    2022

  3. [3]

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

    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 netw orks,” IEEE Commun. Surveys Tut. , vol. 27, no. 2, pp. 748–781, 2025

    work page 2025

  4. [4]

    Cooperative integrated sensing and communication in 6G: From operators perspective,

    X. Wang, Z. Han, R. Xi, G. Liu, L. Han, J. Jin, Y . Xue, L. Ma, Y . Wang, T. Jiang, M. Lou, Q. Wang, and J. Wang, “Cooperative integrated sensing and communication in 6G: From operators perspective,” IEEE Wireless Commun., vol. 32, no. 1, pp. 52–59, 2025

    work page 2025

  5. [5]

    Coop erative ISAC networks: Opportunities and challenges,

    K. Meng, C. Masouros, A. P . Petropulu, and L. Hanzo, “Coop erative ISAC networks: Opportunities and challenges,” IEEE Wireless Commun., pp. 1–8, 2024, to be published, doi: 10.1109/MWC.008.24001 51

    work page doi:10.1109/mwc.008.24001 2024

  6. [6]

    Integrated sensing an d communi- cation: A network level perspective,

    Y . Cui, H. Ding, L. Zhao, and J. An, “Integrated sensing an d communi- cation: A network level perspective,” IEEE Wireless Commun. , vol. 31, no. 1, pp. 103–109, 2024

    work page 2024

  7. [7]

    Networked integrated sensing and communications for 6G wi reless systems,

    J. Li, X. Shao, F. Chen, S. Wan, C. Liu, Z. Wei, and D. Wing Kw an Ng, “Networked integrated sensing and communications for 6G wi reless systems,” IEEE Internet Things J. , vol. 11, no. 17, pp. 29 062–29 075, 2024

    work page 2024

  8. [8]

    Dynamic RAN slicing for service-oriented vehicular netwo rks via constrained learning,

    W. Wu, N. Chen, C. Zhou, M. Li, X. Shen, W. Zhuang, and X. Li, “Dynamic RAN slicing for service-oriented vehicular netwo rks via constrained learning,” IEEE J. Sel. Areas Commun. , vol. 39, no. 7, pp. 2076–2089, 2021

    work page 2021

Show all 43 references
  1. [9]

    Digital twi n- empowered network planning for multi-tier computing,

    C. Zhou, J. Gao, M. Li, X. Shen, and W. Zhuang, “Digital twi n- empowered network planning for multi-tier computing,” J. Commun. Inf. Netw., vol. 7, no. 3, pp. 221–238, 2022

    2022

  2. [10]

    Time allocation approaches for a perceptive mobile network using integrati on of sensing and communication,

    H. Zhang, Y . Zhang, X. Liu, C. Ren, H. Li, and C. Sun, “Time allocation approaches for a perceptive mobile network using integrati on of sensing and communication,” IEEE Trans. Wireless Commun., vol. 23, no. 2, pp. 1158–1169, 2024

    2024

  3. [11]

    Rethink- ing dense cells for integrated sensing and communications: A stochastic geometric view,

    A. Salem, K. Meng, C. Masouros, F. Liu, and D. Lopez-Pere z, “Rethink- ing dense cells for integrated sensing and communications: A stochastic geometric view,” IEEE Open J. Commun. Soc. , vol. 5, pp. 2226–2239, 2024

    2024

  4. [12]

    Performan ce analysis of integrated sensing and communication networks with blocka ge effects,

    Z. Sun, S. Y an, N. Jiang, J. Zhou, and M. Peng, “Performan ce analysis of integrated sensing and communication networks with blocka ge effects,” IEEE Trans. V eh. Technol., vol. 73, no. 11, pp. 16 876–16 891, 2024

    2024

  5. [13]

    Network-leve l integrated sensing and communication: Interference management and BS coordina- tion using stochastic geometry,

    K. Meng, C. Masouros, G. Chen, and F. Liu, “Network-leve l integrated sensing and communication: Interference management and BS coordina- tion using stochastic geometry,” IEEE Trans. Wireless Commun., vol. 23, no. 12, pp. 19 365–19 381, 2024

    2024

  6. [14]

    Model drift in dy namic networks,

    D. M. Manias, A. Chouman, and A. Shami, “Model drift in dy namic networks,” IEEE Commun. Mag. , vol. 61, no. 10, pp. 78–84, 2023

    2023

  7. [15]

    Holi stic network virtualization and pervasive network intelligenc e for 6G,

    X. Shen, J. Gao, W. Wu, M. Li, C. Zhou, and W. Zhuang, “Holi stic network virtualization and pervasive network intelligenc e for 6G,” IEEE Commun. Surveys Tuts. , vol. 24, no. 1, pp. 1–30, Firstquarter 2022

    2022

  8. [16]

    Intellig ent resource adaptation for diversified service requirements in industr ial iot,

    W. Zhang, Y . He, T. Zhang, C. Ying, and J. Kang, “Intellig ent resource adaptation for diversified service requirements in industr ial iot,” IEEE Trans. Cogn. Commun. Netw. , pp. 1–1, 2024

    2024

  9. [17]

    MOTO: Mobility-aware online task offloading with adaptive load balancing in small-cell MEC,

    S. Duan, F. Lyu, H. Wu, W. Chen, H. Lu, Z. Dong, and X. Shen, “MOTO: Mobility-aware online task offloading with adaptive load balancing in small-cell MEC,” IEEE Trans. Mobile Comput. , vol. 23, no. 1, pp. 645–659, 2024

    2024

  10. [18]

    LeaD: Large-scale edge cache deployment based on spatio-t emporal WiFi traffic statistics,

    F. Lyu, J. Ren, N. Cheng, P . Y ang, M. Li, Y . Zhang, and X. Sh en, “LeaD: Large-scale edge cache deployment based on spatio-t emporal WiFi traffic statistics,” IEEE Trans. Mobile Comput. , vol. 20, no. 8, pp. 2607–2623, 2021

    2021

  11. [19]

    Sens ing as a service in 6G perceptive networks: A unified framework for I SAC resource allocation,

    F. Dong, F. Liu, Y . Cui, W. Wang, K. Han, and Z. Wang, “Sens ing as a service in 6G perceptive networks: A unified framework for I SAC resource allocation,” IEEE Trans. Wireless Commun. , vol. 22, no. 5, pp. 3522–3536, 2023

    2023

  12. [20]

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

    Z. He, W. Xu, H. Shen, D. W. K. Ng, Y . C. Eldar, and X. Y ou, “Full-duplex communication for ISAC: Joint beamforming an d power optimization,” IEEE J. Sel. Areas Commun , vol. 41, no. 9, pp. 2920– 2936, 2023

    2023

  13. [21]

    Impact of channel agi ng on dual-function radar-communication systems: Performance analysis and resource allocation,

    J. Chen, X. Wang, and Y .-C. Liang, “Impact of channel agi ng on dual-function radar-communication systems: Performance analysis and resource allocation,” IEEE Trans. Commun. , vol. 71, no. 8, pp. 4972– 4987, 2023

    2023

  14. [22]

    New trends in stochastic geometry for wireless netw orks: A tutorial and survey,

    Y . Hmamouche, M. Benjillali, S. Saoudi, H. Y anikomerog lu, and M. D. Renzo, “New trends in stochastic geometry for wireless netw orks: A tutorial and survey,” Proc. IEEE , vol. 109, no. 7, pp. 1200–1252, 2021

    2021

  15. [23]

    Stochastic geome try interference analysis of radar network performance,

    A. Munari, L. Simi´ c, and M. Petrova, “Stochastic geome try interference analysis of radar network performance,” IEEE Commun. Lett. , vol. 22, no. 11, pp. 2362–2365, 2018

    2018

  16. [24]

    Stochasti c network calculus analysis of spatial-temporal integrated sensing and communication networks,

    M. Mei, M. Y ao, Q. Y ang, J. Wang, and R. R. Rao, “Stochasti c network calculus analysis of spatial-temporal integrated sensing and communication networks,” IEEE Trans. V eh. Technol. , vol. 73, no. 6, pp. 9120–9124, 2024

    2024

  17. [25]

    Sensing-assisted robust SWIP T for mobile energy harvesting receivers in networked ISAC systems,

    Y . Xu, D. Xu, and S. Song, “Sensing-assisted robust SWIP T for mobile energy harvesting receivers in networked ISAC systems,” IEEE Trans. Wireless Commun., vol. 24, no. 3, pp. 2094–2109, 2025

    2025

  18. [26]

    Adaptive devic e- edge collaboration on DNN inference in AIoT: A digital twin- assisted approach,

    S. Hu, M. Li, J. Gao, C. Zhou, and X. Shen, “Adaptive devic e- edge collaboration on DNN inference in AIoT: A digital twin- assisted approach,” IEEE Internet Things J. , vol. 11, no. 7, pp. 12 893–12 908, 2024

    2024

  19. [27]

    Digital-twi n-empowered resource allocation for on-demand collaborative sensing,

    M. Li, J. Gao, C. Zhou, L. Zhao, and X. Shen, “Digital-twi n-empowered resource allocation for on-demand collaborative sensing, ” IEEE Internet Things J. , vol. 11, no. 23, pp. 37 942–37 958, 2024

    2024

  20. [28]

    Network digital twin: Context, enabling technologies, an d opportuni- ties,

    P . Almasan, M. Ferriol-Galm´ es, J. Paillisse, J. Su´ arez-V arela, D. Perino, D. L ´ opez, A. A. P . Perales, P . Harvey, L. Ciavaglia, L. Wong, V . Ram, S. Xiao, X. Shi, X. Cheng, A. Cabellos-Aparicio, and P . Barle t-Ros, “Network digital twin: Context, enabling technologi...

    2022

  21. [29]

    Digital twin for optimization of slicing-enabled c ommunication networks: A federated graph learning approach,

    M. Abdel-Basset, H. Hawash, K. M. Sallam, I. Elgendi, an d K. Munas- inghe, “Digital twin for optimization of slicing-enabled c ommunication networks: A federated graph learning approach,” IEEE Commun. Mag. , vol. 61, no. 10, pp. 100–106, 2023. 15

    2023

  22. [30]

    A graph neural networ k- based digital twin for network slicing management,

    H. Wang, Y . Wu, G. Min, and W. Miao, “A graph neural networ k- based digital twin for network slicing management,” IEEE Trans. Ind. Informat., vol. 18, no. 2, pp. 1367–1376, Feb. 2022

    2022

  23. [31]

    Adaptive se nsing for Internet of robotic things platforms with integrated sensi ng, computing and communication capabilities,

    X. Wang, L. Nkenyereye, S. Rani, and J. Lyu, “Adaptive se nsing for Internet of robotic things platforms with integrated sensi ng, computing and communication capabilities,” IEEE Internet Things J. , 2024

    2024

  24. [32]

    Optim al scheduling policy for time-division joint radar and communication sys tems: Cross- layer design and sensing for free,

    Z. Xie, R. Li, Z. Jiang, J. Zhu, X. She, and P . Chen, “Optim al scheduling policy for time-division joint radar and communication sys tems: Cross- layer design and sensing for free,” IEEE Internet Things J. , vol. 10, no. 23, pp. 20 746–20 760, 2023

    2023

  25. [33]

    Optimal cross-layer scheduling of transmissions over a fading multiaccess channel,

    M. Goyal, A. Kumar, and V . Sharma, “Optimal cross-layer scheduling of transmissions over a fading multiaccess channel,” IEEE Trans. Inf. Theory, vol. 54, no. 8, pp. 3518–3537, 2008

    2008

  26. [34]

    Resource s cheduling for distributed multi-target tracking in netted colocated MIMO radar systems,

    W. Yi, Y . Y uan, R. Hoseinnezhad, and L. Kong, “Resource s cheduling for distributed multi-target tracking in netted colocated MIMO radar systems,” IEEE Trans. Signal Process. , vol. 68, pp. 1602–1617, 2020

    2020

  27. [35]

    Per formance analysis of uncoordinated interference mitigation for aut omotive radar,

    Y . Wang, Q. Zhang, Z. Wei, L. Kui, F. Liu, and Z. Feng, “Per formance analysis of uncoordinated interference mitigation for aut omotive radar,” IEEE Trans. V eh. Technol., vol. 72, no. 4, pp. 4222–4235, 2023

    2023

  28. [36]

    Adaptive sche duling for joint communication and radar detection: Tradeoff among th roughput, delay, and detection performance,

    H. Ju, Y . Long, X. Fang, Y . Fang, and R. He, “Adaptive sche duling for joint communication and radar detection: Tradeoff among th roughput, delay, and detection performance,” IEEE Trans. V eh. Technol., vol. 71, no. 1, pp. 670–680, 2022

    2022

  29. [37]

    Beamwidth optimization and resource pa rtitioning scheme for localization assisted mm-wave communication,

    G. Ghatak, R. Koirala, A. De Domenico, B. Denis, D. Darda ri, B. Uguen, and M. Coupechoux, “Beamwidth optimization and resource pa rtitioning scheme for localization assisted mm-wave communication,” IEEE Trans. Commun., vol. 69, no. 2, pp. 1358–1374, 2021

    2021

  30. [38]

    Integ rated sensing and communications: Recent advances and ten open ch allenges,

    S. Lu, F. Liu, Y . Li, K. Zhang, H. Huang, J. Zou, X. Li, Y . Do ng, F. Dong, J. Zhu, Y . Xiong, W. Y uan, Y . Cui, and L. Hanzo, “Integ rated sensing and communications: Recent advances and ten open ch allenges,” IEEE Internet Things J. , vol. 11, no. 11, pp. 19 094–19 120, 2024

    2024

  31. [39]

    To- ward ambient intelligence: Federated edge learning with ta sk-oriented sensing, computation, and communication integration,

    P . Liu, G. Zhu, S. Wang, W. Jiang, W. Luo, H. V . Poor, and S. Cui, “To- ward ambient intelligence: Federated edge learning with ta sk-oriented sensing, computation, and communication integration,” IEEE J. Sel. Topics Signal Process. , vol. 17, no. 1, pp. 158–172, 2023

    2023

  32. [40]

    Implementing speed and se paration monitoring in collaborative robot workcells,

    J. A. Marvel and R. Norcross, “Implementing speed and se paration monitoring in collaborative robot workcells,” Robot. Comput. Integr . Manuf., vol. 44, pp. 144–155, 2017

    2017

  33. [41]

    Spatio- temporal point process statistics: a review,

    J. A. Gonz´ alez, F. J. Rodr´ ıguez-Cort´ es, O. Cronie, and J. Mateu, “Spatio- temporal point process statistics: a review,” Spatial Statist. , vol. 18, pp. 505–544, 2016

    2016

  34. [42]

    Modeling and cove rage analysis of BS-centric clustered users in a random wireless network,

    P . D. Mankar, G. Das, and S. S. Pathak, “Modeling and cove rage analysis of BS-centric clustered users in a random wireless network, ” IEEE Wireless Commun. Lett. , vol. 5, no. 2, pp. 208–211, 2016

    2016

  35. [43]

    S tochastic geometry analysis of spatial-temporal performance in wire less networks: A tutorial,

    X. Lu, M. Salehi, M. Haenggi, E. Hossain, and H. Jiang, “S tochastic geometry analysis of spatial-temporal performance in wire less networks: A tutorial,” IEEE Commun. Surveys Tuts. , vol. 23, no. 4, pp. 2753–2801, 2021

    2021

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.