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REVIEW 3 major objections 3 minor 50 references

Joint Sensing and Bi-Directional Communication with Dynamic TDD Enabled Cell-Free MIMO

T0 review · 3 major / 3 minor · reviewed 2026-08-06 · deepseek-v4-flash

Pith's one-line read Dynamic TDD cell-free MIMO doubles the 90th-percentile UL-DL spectral efficiency in ISAC systems while using half-duplex access points.

desk verdict The submitted full text is a different paper, so the ISAC claims are untestable; the correct manuscript must be obtained before any reviewer looks at it. read the letter →

arxiv 2508.03460 v1 pith:SYELY2UC submitted 2025-08-05 eess.SP

classification eess.SP
keywords dynamictimedivisionduplexcell-freemassiveMIMOintegratedsensingandcommunicationgeneralizedlikelihood-ratiotesthalf-duplexaccesspointsspectralefficiencyradarcross-sectionestimationuplink-downlinkcoexistence
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 argues that dynamic time division duplex (DTDD) turns a cell-free massive MIMO network into a joint sensing and communication system without needing full-duplex hardware: spatially separated half-duplex access points can serve uplink and downlink users on the same time-frequency resources while some of them simultaneously receive target echoes. The paper develops centralized and distributed generalized likelihood-ratio tests (GLRTs) for detecting a target when uplink user signals are treated as sensing interference, and it benchmarks radar cross-section estimators against the Bayesian Cramér-Rao lower bound. It also derives an SINR-optimal uplink combiner that accounts for cross-link and radar interference, and two downlink precoders for the target signal, one protecting downlink users and one maximizing illumination of the target. Numerical results are claimed to show the GLRT is robust to inter-AP interference and that DTDD doubles the 90%-likely sum uplink-downlink spectral efficiency relative to traditional TDD-based cell-free ISAC, on half-duplex hardware.

What carries the argument

The load-bearing machinery is the dynamic TDD schedule itself, which partitions APs into uplink and downlink groups whose simultaneous transmissions are made coherent by spatial separation. On top of it sit the generalized likelihood-ratio tests (GLRTs), one centralized and one distributed, that treat UL user signals as sensing interference; the SINR-optimal uplink combiner that suppresses cross-link and radar interference; and the two target precoders: a user-centric precoder that nulls interference to DL users and a target-centric precoder built on the dominant eigenvector of the composite target-AP channel. Radar cross-section estimation is benchmarked against the Bayesian Cramér-Rao lower bound.

What would settle it

Run the same comparison of DTDD versus conventional TDD cell-free ISAC with inter-AP interference at realistic levels, for instance with APs placed in a dense topology or with correlated shadowing; if the claimed doubling of the 90%-likely sum UL-DL spectral efficiency disappears, or if a GLRT detection probability drops materially when inter-AP interference is strong, the central claim fails. A field measurement of UL-DL coexistence would settle it directly.

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Extended reading notes

Core claim

The central claim is that DTDD lets a cell-free massive MIMO ISAC system break the TDD orthogonality between uplink and downlink without full-duplex radios: by assigning different half-duplex APs to uplink and downlink and by letting uplink APs double as echo receivers, the same time-frequency resource carries UL data, DL data, and target sensing simultaneously. The paper's headline numerical finding is that this arrangement doubles the 90%-likely sum UL-DL spectral efficiency compared with a conventional TDD-based CF-mMIMO ISAC baseline, while the proposed GLRT detectors remain robust to inter-AP interference. The authors support this with a unified framework that jointly detects UL user data and estimates target RCS, an SINR-optimal UL combiner, and two DL precoding strategies for the target signal.

Load-bearing premise

Everything hinges on the assumption that spatially separating uplink and downlink half-duplex APs keeps simultaneous same-frequency transmissions from interfering destructively, and that the simulation framework used to compute the 90%-likely spectral efficiency gain faithfully represents real channel conditions.

Editorial extensions

If this is right

  • Half-duplex access points suffice for simultaneous uplink, downlink, and sensing, so full-duplex hardware is not required to realize the gain.
  • The robustness of the GLRT to inter-AP interference makes distributed detection viable with reduced backhaul demand.
  • The user-centric and target-centric precoders give network designers a trade-off between protecting downlink users and concentrating energy on the target.
  • The unified UL data detection and RCS estimation framework offers a path to joint communication and sensing performance benchmarking against the Bayesian Cramér-Rao lower bound.

Reading between the lines

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

  • The doubling result may hinge on the specific AP-user geometry of the simulation; deployments where uplink and downlink APs are not well separated could see a smaller gain.
  • A natural extension the authors leave implicit is joint optimization of the AP mode assignment, which APs go uplink versus downlink, together with transmit powers and precoders.
  • The two target precoders likely form a Pareto frontier; one could interpolate between them to balance user protection and target illumination.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 3 minor

Summary. The submission carries the title 'Joint Sensing and Bi-Directional Communication with Dynamic TDD Enabled Cell-Free MIMO' and an abstract describing centralized and distributed GLRTs for target detection, a Bayesian Cramér-Rao bound for RCS estimation, an SINR-optimal combiner, two target precoders, and a numerical claim that DTDD doubles the 90%-likely sum UL-DL SE relative to traditional TDD-based CF-mMIMO ISAC. However, the full text supplied is a completely different manuscript, 'Learning to Incentivize: LLM-Empowered Contract for AIGC Offloading in Teleoperation' (arXiv:2508.03464), which contains no ISAC, cell-free MIMO, dynamic TDD, or radar content. None of the claimed technical derivations, system models, or simulation results appear anywhere in the provided text.

Significance. The claimed ISAC contributions, if they were present and correct, would be of interest to the cell-free massive MIMO and integrated sensing and communication communities, particularly the reported DTDD spectral-efficiency doubling and the robustness of the GLRT to inter-AP interference. The submitted manuscript, however, provides no evidence for these claims: the full text is an unrelated paper on LLM-empowered contract design for AIGC offloading. The significance of the claimed contributions cannot be assessed from the submitted text, and the manuscript in its current form makes no verifiable contribution to the ISAC literature.

major comments (3)
  1. [Abstract vs. Full Text] The abstract promises a paper on joint sensing and bi-directional communication with dynamic TDD cell-free MIMO, including centralized and distributed GLRTs, Bayesian CRB, SINR-optimal combiners, and two precoders. The full text is a paper titled 'Learning to Incentivize: LLM-Empowered Contract for AIGC Offloading in Teleoperation,' whose abstract, sections, equations, and simulations concern teleoperation, diffusion models, and contract theory. None of the claimed ISAC content appears anywhere in the submitted full text.
  2. [Sec. 3 and Sec. 5] All load-bearing technical claims are unsupported: the GLRT derivations, the Bayesian CRB, the SINR-optimal combiner, the target precoders, and the numerical result that DTDD doubles the 90%-likely sum UL-DL SE require a system model, signal equations, channel and CSI assumptions, and simulations. The provided Sec. 3 formulates a contract-design optimization problem (P1–P3), and Sec. 5 reports teleoperator utility and AIGC service quality experiments; neither section contains any ISAC or cell-free MIMO mathematics.
  3. [Numerical Studies] The abstract's central quantitative claim that 'DTDD doubles the 90%-likely sum UL-DL SE compared to traditional TDD-based CF-mMIMO ISAC systems' is not backed by any simulation in the supplied full text. The numerical results in the provided manuscript concern utility improvements of 5–40% for a teleoperator and contract-design benchmarks, not spectral efficiency. The claimed performance result is therefore unverifiable from the submitted document.
minor comments (3)
  1. [Document identity] The arXiv identifier shown in the full text (arXiv:2508.03464) does not match the submission identifier (arXiv:2508.03460), which is consistent with the document-identity mismatch between the abstract and the full text.
  2. [Title and author list] The full text's title, author list, and abstract differ entirely from those of the submission; the journal header 'JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015' and the GitHub code link are those of the LLM contract paper, not of an ISAC paper.
  3. [References and index terms] The index terms and references in the supplied text (e.g., teleoperation, large language model, contract theory, and references [4], [5], [22]–[26]) belong to the LLM contract-design paper; the expected ISAC/cell-free MIMO references are absent.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity can be established because the supplied full text is an unrelated paper (arXiv:2508.03464), so the claimed DTDD/ISAC derivation chain of arXiv:2508.03460 is absent and cannot be audited for circular equivalence.

full rationale

The manuscript supplied under the requested identifier is not the claimed paper: it is 'Learning to Incentivize: LLM-Empowered Contract for AIGC Offloading in Teleoperation' (arXiv:2508.03464), a contract-theoretic study of AIGC offloading. None of the abstract's load-bearing components for arXiv:2508.03460 appear in the supplied text: there is no DTDD system model, no GLRT derivation, no Bayesian CRB, no SINR-optimal combiner, no target precoder, and no simulation producing the stated 90%-likely sum UL-DL SE doubling. I therefore examined the supplied text itself for circularity. Its inference pipeline (P2 setting inference followed by P3 contract derivation) does not contain any step in which a predicted quantity equals its own input by construction: the seed solver infers the ASP setting from historical interaction logs, and the LLM-evolved solver is scored on the simulated teleoperator utility, not on the inferred setting itself. The efficiency metric in Eq. (12) is defined as eta = pi_T(r_tilde)/pi_T(r_star), where r_star is the optimal contract computed under the true setting Phi; this is an external benchmark rather than a circular target. The paper also compares against bandit and seed baselines with independently generated data, so the reported 5-40% utility gain is not forced by a fitting identity. The central claims of arXiv:2508.03460 remain unverifiable because the wrong full text was provided, but absence of the derivation is not itself circular reasoning. Under the hard rule that circularity must be exhibited by quotation and a specific reduction, no circular step can be asserted, and the appropriate score is 0. Re-review with the correct manuscript is required to audit the ISAC-derived claims.

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

The submitted full text does not contain the ISAC work described in the abstract. No free parameters, axioms, or invented entities can be audited from the abstract alone. The full text of the submission is an unrelated paper on LLM-based contract design, so no technical assumptions of the claimed ISAC paper are available for review.

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Pith. "Pith review of Joint Sensing and Bi-Directional Communication with Dynamic TDD Enabled Cell-Free MIMO." pith.science (2026). https://pith.science/paper/SYELY2UC

@misc{pith2026250803460,
  author       = {Pith},
  title        = {Pith review of: Joint Sensing and Bi-Directional Communication with Dynamic TDD Enabled Cell-Free MIMO},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SYELY2UC}},
  note         = {Machine review of arXiv:2508.03460}
}
abstract

This paper studies integrated sensing and communication (ISAC) with dynamic time division duplex (DTDD) cell-free (CF) massive multiple-input multiple-output~(mMIMO) systems. DTDD enables the CF mMIMO system to concurrently serve both uplink~(UL) and downlink~(DL) users with spatially separated \emph{half-duplex~(HD)} access points~(APs) using the same time-frequency resources. Further, to facilitate ISAC, the UL APs are utilized for both UL data and target echo reception, while the DL APs jointly transmit the precoded DL data streams and target signal. In this context, we present centralized and distributed generalized likelihood-ratio tests~(GLRTs) for target detection treating UL users' signals as sensing interference. We then quantify the optimality and complexity trade-off between distributed and centralized GLRTs and benchmark the respective estimators with the Bayesian Cram\'er-Rao lower bound for target radar-cross section~(RCS). Then, we present a unified framework for joint UL users' data detection and RCS estimation. Next, for communication, we derive the signal-to-noise-plus-interference~(SINR) optimal combiner accounting for the cross-link and radar interference for UL data processing. In DL, we use regularized zero-forcing for the users and propose two types of precoders for the target: one ``user-centric" that nullifies the interference caused by the target signal to the DL users and one ``target-centric" based on the dominant eigenvector of the composite channel between the target and the APs. Finally, numerical studies corroborate with our theoretical findings and reveal that the \emph{GLRT is robust to inter-AP interference, and DTDD doubles the $90\%$-likely sum UL-DL SE compared to traditional TDD-based CF-mMIMO ISAC systems}; while using HD hardware.

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Works this paper leans on

50 extracted references · 49 canonical work pages

  1. [1]

    Global artificial intelligence generated content (aigc) market size by application, by technology, by end-user industry, by geographic scope and forecast,

    V . M. Research, “Global artificial intelligence generated content (aigc) market size by application, by technology, by end-user industry, by geographic scope and forecast,” https://www.verifiedmarketresearch.com/product/artificial- intelligence-generated-content-aigc-market, Jan. 2024, (Accessed 20 June 2025)

  2. [2]

    Empowering education devel- opment through aigc: A systematic literature review,

    X. Chen, Z. Hu, and C. Wang, “Empowering education devel- opment through aigc: A systematic literature review,” Educ. Inf. Technol., vol. 29, no. 13, pp. 17 485–17 537, Feb 2024

  3. [3]

    A revolution of personalized healthcare: Enabling human digital twin with mobile aigc,

    J. Chen, C. Yi, H. Du, D. Niyato, J. Kang, J. Cai, and X. Shen, “A revolution of personalized healthcare: Enabling human digital twin with mobile aigc,” IEEE Netw., vol. 38, no. 6, pp. 234–242, Nov 2024

  4. [4]

    Vision lan- guage model-empowered contract theory for aigc task allocation in teleoperation,

    Z. Zhan, Y. Dong, Y. Hu, S. Li, S. Cao, and Z. Han, “Vision lan- guage model-empowered contract theory for aigc task allocation in teleoperation,” IEEE Trans. Mob. Comput. , vol. 24, no. 8, pp. 7742–7756, Aug 2025

  5. [5]

    Distributionally Robust Contract Theory for Edge AIGC Services in Teleoperation

    Z. Zhan, Y. Dong, D. M. Doe, Y. Hu, S. Li, S. Cao, L. Fan, and Z. Han, “Distributionally robust contract theory for edge aigc services in teleoperation,” arXiv preprint arXiv:2505.06678 , May 2025

  6. [6]

    Network latency in teleoperation of connected and autonomous vehicles: A review of trends, challenges, and mitigation strategies,

    S. B. Kamtam, Q. Lu, F. Bouali, O. C. Haas, and S. Birrell, “Network latency in teleoperation of connected and autonomous vehicles: A review of trends, challenges, and mitigation strategies,” Sensors, vol. 24, no. 12, p. 3957, Jun 2024

  7. [7]

    Diffusion-based reinforcement learning for edge-enabled ai-generated content services,

    H. Du, Z. Li, D. Niyato, J. Kang, Z. Xiong, H. Huang, and S. Mao, “Diffusion-based reinforcement learning for edge-enabled ai-generated content services,” IEEE Trans. Mob. Comput. , vol. 23, no. 9, pp. 8902–8918, Sep 2024

  8. [8]

    Enabling ai-generated content services in wireless edge networks,

    H. Du, Z. Li, D. Niyato, J. Kang, Z. Xiong, X. S. Shen, and D. I. Kim, “Enabling ai-generated content services in wireless edge networks,” IEEE Wirel. Commun. , vol. 31, no. 3, pp. 226–234, Feb 2024. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 14

Show all 50 references
  1. [9]

    Multi- agent rl-based industrial aigc service offloading over wireless edge networks,

    S. Li, X. Lin, H. Xu, K. Hua, X. Jin, G. Li, and J. Li, “Multi- agent rl-based industrial aigc service offloading over wireless edge networks,” in IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Vancouver, Canada, May. 2024

  2. [10]

    Potential game-based computation offloading in edge computing with heterogeneous edge servers,

    Z. Zhou, L. Pan, and S. Liu, “Potential game-based computation offloading in edge computing with heterogeneous edge servers,” IEEE Trans. Netw. Sci. Eng., vol. 12, no. 1, pp. 290–301, Jan-Feb 2025

  3. [11]

    Revenue-oriented optimal service offloading based on fog-cloud collaboration in sd-wan enabled manufacturing networks,

    X. Chen, Y. Zhang, C. Jiang, C. Xu, Z. Yuan, and G.-M. Muntean, “Revenue-oriented optimal service offloading based on fog-cloud collaboration in sd-wan enabled manufacturing networks,” IEEE Trans. Netw. Sci. Eng., vol. 12, no. 2, pp. 1237–1249, Mar-Apr 2025

  4. [12]

    Diffusion model-based incentive mechanism with prospect the- ory for edge aigc services in 6g iot,

    J. Wen, J. Nie, Y. Zhong, C. Yi, X. Li, J. Jin, Y. Zhang, and D. Niyato, “Diffusion model-based incentive mechanism with prospect the- ory for edge aigc services in 6g iot,” IEEE Internet Things J., vol. 11, no. 21, pp. 34 187–34 201, Nov 2024

  5. [13]

    Freshness-aware incentive mechanism for mobile ai-generated content (aigc) networks,

    J. Wen, J. Kang, M. Xu, H. Du, Z. Xiong, Y. Zhang, and D. Niyato, “Freshness-aware incentive mechanism for mobile ai-generated content (aigc) networks,” in IEEE/CIC International Conference on Communications in China (ICCC), Dalian, China, Aug. 2023

  6. [14]

    Learning-based big data sharing incentive in mobile aigc networks,

    J. Wen, Y. Zhang, Y. Chen, W. Zhong, X. Huang, L. Liu, and D. Niyato, “Learning-based big data sharing incentive in mobile aigc networks,” in IEEE Global Communications Conference (GLOBE- COM), Cape Town, South Africa, Dec. 2024

  7. [15]

    Opti- mizing aigc services by prompt engineering and edge computing: A generative diffusion model-based contract theory approach,

    D. Ye, S. Cai, H. Du, J. Kang, Y. Liu, R. Yu, and D. Niyato, “Opti- mizing aigc services by prompt engineering and edge computing: A generative diffusion model-based contract theory approach,” IEEE Trans. Veh. Technol., vol. 74, no. 1, pp. 571–586, Jan 2025

  8. [16]

    J. Li, D. Niyato, and Z. Han, Cryptoeconomics: Economic Mechanisms behind Blockchains. Cambridge University Press, 2023

  9. [17]

    The unreasonable effectiveness of deep features as a perceptual metric,

    R. Zhang, P . Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, Jun. 2018

  10. [18]

    Image quality assessment: From error visibility to structural similarity,

    Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P . Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, Apr 2004

  11. [19]

    Contract- theoretic pricing for security deposits in sharded blockchain with internet of things (iot),

    J. Li, T. Liu, D. Niyato, P . Wang, J. Li, and Z. Han, “Contract- theoretic pricing for security deposits in sharded blockchain with internet of things (iot),” IEEE Internet Things J. , vol. 8, no. 12, pp. 10 052–10 070, Jun 2021

  12. [20]

    Promoting the sustainability of blockchain in web 3.0 and the metaverse through diversified incentive mechanism design,

    D. M. Doe, J. Li, N. Dusit, Z. Gao, J. Li, and Z. Han, “Promoting the sustainability of blockchain in web 3.0 and the metaverse through diversified incentive mechanism design,” IEEE Open J. Comput. Soc., vol. 4, pp. 171–184, Mar 2023

  13. [21]

    Prosecutor: Protecting mobile aigc services on two-layer blockchain via reputation and contract theoretic approaches,

    Y. Liu, H. Du, D. Niyato, J. Kang, Z. Xiong, A. Jamalipour, and X. Shen, “Prosecutor: Protecting mobile aigc services on two-layer blockchain via reputation and contract theoretic approaches,” IEEE Trans. Mob. Comput. , vol. 23, no. 12, pp. 10 966–10 983, Apr 2024

  14. [22]

    Adaptive contract design for crowdsourcing markets: Bandit algorithms for repeated principal-agent problems,

    C.-J. Ho, A. Slivkins, and J. W. Vaughan, “Adaptive contract design for crowdsourcing markets: Bandit algorithms for repeated principal-agent problems,” in Proceedings of the fifteenth ACM con- ference on Economics and computation, Palo Alto, CA, Jun. 2014

  15. [23]

    Simple versus optimal contracts,

    P . D ¨utting, T. Roughgarden, and I. Talgam-Cohen, “Simple versus optimal contracts,” in Proceedings of the ACM Conference on Eco- nomics and Computation, Phoenix, AZ, Jun. 2019

  16. [24]

    Learn- ing optimal contracts: How to exploit small action spaces,

    F. Bacchiocchi, M. Castiglioni, A. Marchesi, and N. Gatti, “Learn- ing optimal contracts: How to exploit small action spaces,” in International Conference on Learning Representations (ICLR) , Vienna, Austria, May. 2024

  17. [25]

    Contracting with a learning agent,

    G. Guruganesh, Y. Kolumbus, J. Schneider, I. Talgam-Cohen, E.- V . Vlatakis-Gkaragkounis, J. Wang, and S. Weinberg, “Contracting with a learning agent,” in Advances in Neural Information Processing Systems (NeurIPS), Vancouver, Canada, Dec. 2024

  18. [26]

    Deep contract design via discontinuous networks,

    T. Wang, P . D¨utting, D. Ivanov, I. Talgam-Cohen, and D. C. Parkes, “Deep contract design via discontinuous networks,” in Advances in Neural Information Processing Systems (NeurIPS) , New Orleans, LA, Dec. 2023

  19. [27]

    Evolution of heuristics: Towards efficient automatic algorithm design using large language model,

    F. Liu, X. Tong, M. Yuan, X. Lin, F. Luo, Z. Wang, Z. Lu, and Q. Zhang, “Evolution of heuristics: Towards efficient automatic algorithm design using large language model,” inProceedings of the 41st International Conference on Machine Learning (ICML) , Vienna, Austria, Jul. 2024

  20. [28]

    Reevo: Large language models as hyper-heuristics with reflective evolution,

    H. Ye, J. Wang, Z. Cao, F. Berto, C. Hua, H. Kim, J. Park, and G. Song, “Reevo: Large language models as hyper-heuristics with reflective evolution,” in Advances in Neural Information Processing Systems (NeurIPS), Vancouver, Canada, Dec. 2024

  21. [29]

    Multi- objective evolution of heuristic using large language model,

    S. Yao, F. Liu, X. Lin, Z. Lu, Z. Wang, and Q. Zhang, “Multi- objective evolution of heuristic using large language model,” in Proceedings of the AAAI Conference on Artificial Intelligence, Philadel- phia, PA, Feb-Mar. 2025

  22. [30]

    O. D. Hart and B. Holmstrm, The theory of contracts . MIT press, 1986

  23. [31]

    The theory of

    J. E. Stiglitz, “The theory of” screening,” education, and the distri- bution of income,” The American economic review, vol. 65, no. 3, pp. 283–300, Jun 1975

  24. [32]

    Moral hazard and observability,

    B. Holmstr ¨om, “Moral hazard and observability,” The Bell journal of economics, vol. 10, no. 1, pp. 74–91, Spring 1979

  25. [33]

    Bolton and M

    P . Bolton and M. Dewatripont, Contract theory. MIT press, 2004

  26. [34]

    Incentive mechanism design for two-layer wireless edge caching networks using contract theory,

    T. Liu, J. Li, F. Shu, H. Guan, Y. Wu, and Z. Han, “Incentive mechanism design for two-layer wireless edge caching networks using contract theory,” IEEE Trans. Serv. Comput., vol. 14, no. 5, pp. 1426–1438, Sep-Oct 2021

  27. [35]

    Contract-based in- centive design for resource allocation in edge computing-based blockchain,

    Z. Yu, Z. Chang, L. Wang, and G. Min, “Contract-based in- centive design for resource allocation in edge computing-based blockchain,” IEEE Trans. Netw. Sci. Eng. , vol. 11, no. 6, pp. 6143– 6156, Nov-Dec 2024

  28. [36]

    Con- sortium blockchain for secure resource sharing in vehicular edge computing: A contract-based approach,

    S. Wang, D. Ye, X. Huang, R. Yu, Y. Wang, and Y. Zhang, “Con- sortium blockchain for secure resource sharing in vehicular edge computing: A contract-based approach,” IEEE Trans. Netw. Sci. Eng., vol. 8, no. 2, pp. 1189–1201, Apr-Jun 2021

  29. [37]

    Motilearn: Contract- based incentive mechanism for heterogeneous edge collaborative training,

    Q. Wang, S. Guo, G. Liu, L. Yang, and C. Pan, “Motilearn: Contract- based incentive mechanism for heterogeneous edge collaborative training,” IEEE Trans. Netw. Sci. Eng., vol. 9, no. 4, pp. 2895–2909, Jul-Aug 2022

  30. [38]

    Zhang and Z

    Y. Zhang and Z. Han, Contract theory for wireless networks . Springer, 2017

  31. [39]

    Multi-dimensional payment plan in fog computing with moral hazard,

    Y. Zhang, N. H. Tran, D. Niyato, and Z. Han, “Multi-dimensional payment plan in fog computing with moral hazard,” in 2016 IEEE International Conference on Communication Systems (ICCS) , Shenzhen, China, Dec. 2016

  32. [40]

    A con- tract theory based incentive mechanism for federated learning,

    M. Tian, Y. Chen, Y. Liu, Z. Xiong, C. Leung, and C. Miao, “A con- tract theory based incentive mechanism for federated learning,” arXiv preprint arXiv:2108.05568, Aug 2021

  33. [41]

    Moral hazard based incentive mechanism for cooperative caching in multi-hop com- munications,

    Y. Ding, L. Wang, Z. Yang, and Z. Han, “Moral hazard based incentive mechanism for cooperative caching in multi-hop com- munications,” in IEEE Globecom Workshops, Singapore, Dec. 2017

  34. [42]

    Multi- dimensional incentive mechanism in mobile crowdsourcing with moral hazard,

    Y. Zhang, Y. Gu, M. Pan, N. H. Tran, Z. Dawy, and Z. Han, “Multi- dimensional incentive mechanism in mobile crowdsourcing with moral hazard,” IEEE Trans. Mob. Comput. , vol. 17, no. 3, pp. 604– 616, Mar 2018

  35. [43]

    Optimal incentive mechanism design for short video collaborative caching system in mobile edge networks based on contract theory,

    Z. Li, Q. Yang, and Z. Li, “Optimal incentive mechanism design for short video collaborative caching system in mobile edge networks based on contract theory,” Peer-to-Peer Networking and Applications, vol. 18, no. 120, pp. 1–12, Mar 2025

  36. [44]

    Enhancing trust and security in the vehicular metaverse: A reputation-based mechanism for participants with moral hazard,

    L. Ismail, M. Qaraqe, A. Ghrayeb, and D. Niyato, “Enhancing trust and security in the vehicular metaverse: A reputation-based mechanism for participants with moral hazard,” in IEEE Wireless Communications and Networking Conference (WCNC), Dubai, United Arab Emirates, Apr. 2024

  37. [45]

    Algorithmic con- tract theory: A survey,

    P . D ¨utting, M. Feldman, I. Talgam-Cohen et al., “Algorithmic con- tract theory: A survey,” Found. Trends Theor. Comput. Sci. , vol. 16, no. 3-4, pp. 211–412, Dec 2024

  38. [46]

    The sample complexity of online contract design,

    B. Zhu, S. Bates, Z. Yang, Y. Wang, J. Jiao, and M. I. Jordan, “The sample complexity of online contract design,” in 24th ACM Conference on Economics and Computation, London, UK, Jul. 2023

  39. [47]

    Learning approximately optimal contracts,

    A. Cohen, A. Deligkas, and M. Koren, “Learning approximately optimal contracts,” in International Symposium on Algorithmic Game Theory, Colchester, UK, Sep. 2022

  40. [48]

    Learn- ing optimal contracts with small action spaces,

    F. Bacchiocchi, M. Castiglioni, N. Gatti, and A. Marchesi, “Learn- ing optimal contracts with small action spaces,” Artificial Intelli- gence, vol. 344, p. 104334, Jul 2025

  41. [49]

    Exploring collaborative distributed diffusion- based ai-generated content (aigc) in wireless networks,

    H. Du, R. Zhang, D. Niyato, J. Kang, Z. Xiong, D. I. Kim, X. S. Shen, and H. V . Poor, “Exploring collaborative distributed diffusion- based ai-generated content (aigc) in wireless networks,” IEEE Netw., vol. 38, no. 3, pp. 178–186, May 2024

  42. [50]

    Unsupervised k-means clustering algorithm,

    K. P . Sinaga and M.-S. Yang, “Unsupervised k-means clustering algorithm,” IEEE Access, vol. 8, pp. 80 716–80 727, Apr 2020

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