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arxiv: 2604.11849 · v1 · submitted 2026-04-13 · 💻 cs.IT · math.IT

Channel-Aware Preemptive Scheduling for Semantic Communication with Truncated Diffusion and Path Compensation

Chengyang Liang , Dong Li This is my paper

Pith reviewed 2026-05-10 16:18 UTC · model grok-4.3

classification 💻 cs.IT math.IT
keywords semantic communicationdiffusion modelspreemptive schedulingchannel-aware transmissiontruncated diffusionpath compensationfast fading channelswireless semantic systems
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The pith

Semantic communication systems can interrupt diffusion generation for early transmission when channels are good and compensate at the receiver to cut latency without losing reconstruction quality.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a scheduling approach for semantic communication that links transmission timing directly to wireless channel quality rather than waiting for full diffusion-based generation. Users track a countdown based on their current channel gain and transmit as soon as the countdown hits zero, even if the diffusion steps are incomplete. At the receiver, a path deficit metric derived from the inverse dynamics velocity field identifies which image blocks need extra recovery effort and applies adaptive weighted inverse sampling to restore semantic content. This integration addresses the mismatch between the iterative delays of diffusion models and the rapid changes in fading channels. If effective, the method lets systems deliver task-relevant features faster while preserving high-fidelity reconstruction.

Core claim

The central claim is that a channel-aware preemptive scheduler, which allows truncation of the forward diffusion process under favorable instantaneous channel gains, combined with a receiver-side path compensation mechanism using a path deficit metric from the inverse dynamics model, reduces end-to-end latency while maintaining semantic reconstruction fidelity in fast-fading environments.

What carries the argument

The channel-driven countdown scheduler, where each user's countdown is set inversely proportional to its instantaneous channel gain, paired with the path deficit metric that quantifies recovery difficulty for image blocks via the velocity field of the inverse dynamics model to guide adaptive weighted inverse sampling.

If this is right

  • End-to-end latency drops because transmission can begin before diffusion completes when channel conditions allow.
  • Semantic reconstruction quality stays high through receiver compensation that targets difficult image blocks.
  • Overall system robustness increases in fast-fading environments by exploiting momentary good channels instead of waiting.
  • Multiple users can share the channel more efficiently since the shortest-countdown user transmits immediately.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same preemptive logic could apply to other multi-step generative processes used in semantic communication beyond diffusion models.
  • Joint design of generation steps and transmission timing may become necessary for latency-sensitive semantic tasks in time-varying channels.
  • The path deficit idea might extend to other receiver-side recovery methods when partial semantic features arrive.

Load-bearing premise

The receiver-side path compensation can recover enough semantic information from truncated diffusion steps to avoid unacceptable quality loss or added computational cost.

What would settle it

A direct comparison, in a fast-fading channel trace, of end-to-end latency and perceptual reconstruction metrics between the proposed preemptive method and a baseline that completes all diffusion steps before any transmission, checking whether quality remains comparable when truncation frequency increases.

Figures

Figures reproduced from arXiv: 2604.11849 by Chengyang Liang, Dong Li.

Figure 1
Figure 1. Figure 1: Illustration of the proposed channel-aware preemptive scheduling with truncated diffusion and path compensation semantic [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of the proposed path compensation mechanism. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of the performance in Cifar-100 datasets with [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of the performance in ImageNet-256 with AWGN [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Illustration of the proposed semantic-latency guided patch-wise reconstruction. [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Cumulative distribution of transmission latency under [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
read the original abstract

Semantic communication (SemCom) presents a transformative paradigm for alleviating bandwidth limitations in mobile networks by transmitting task-relevant semantic features instead of raw data bits. While SemCom systems utilizing diffusion models achieve superior generation quality, existing research treats semantic generation and wireless transmission as temporally independent processes. This separation neglects the intrinsic conflict between the multi-step iterative delays inherent in diffusion models and the time-varying fading characteristics of wireless channels. To address this discrepancy, this paper proposes a channel-aware preemptive scheduling with truncated diffusion and path compensation (CAPS-TDPC) framework. Contrary to conventional methods that require completion of the generation phase prior to transmission, the proposed framework implements a channel-driven scheduling mechanism: each user maintains a countdown inversely proportional to its instantaneous channel gain, and the user with the shortest countdown transmits immediately, regardless of whether its diffusion process has completed. This design permits the interruption of the forward diffusion process to enable early transmission under favorable channel conditions. In addition, a receiver-side compensation mechanism grounded in path dynamics is developed to mitigate the semantic loss resulting from such interruptions. A path deficit metric is proposed at the receiver to quantify the recovery difficulty of distinct image blocks by incorporating the velocity field of the inverse dynamics model, which allows for adaptive weighted inverse sampling. Experimental evaluations demonstrate that the proposed framework substantially reduces the end-to-end latency while maintaining the high-fidelity semantic reconstruction, thereby enhancing the system robustness in fast fading channel environments.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes the CAPS-TDPC framework for semantic communication with diffusion models. It introduces channel-aware preemptive scheduling via per-user countdowns inversely proportional to instantaneous channel gain, permitting early transmission by truncating the forward diffusion process, together with a receiver-side path compensation mechanism that defines a path deficit metric from the inverse dynamics velocity field to drive adaptive weighted inverse sampling and thereby mitigate semantic loss from interruptions.

Significance. If the latency-fidelity claims are substantiated, the work would address a genuine tension between the iterative delays of diffusion-based semantic generation and the time-varying nature of wireless channels, offering a practical route to lower end-to-end latency and improved robustness in fast-fading environments. The explicit coupling of scheduling decisions to channel state and the use of path-dynamics information for compensation represent potentially useful contributions to semantic communication system design.

major comments (2)
  1. [Experimental Evaluations] The abstract states that experimental evaluations demonstrate substantial end-to-end latency reduction while maintaining high-fidelity semantic reconstruction, yet no simulation setup, baseline algorithms, error bars, data exclusion criteria, or statistical significance tests are described. This information is load-bearing for the central performance claim and must be supplied with sufficient detail for independent verification.
  2. [Path Compensation Mechanism] The receiver-side path compensation mechanism, which relies on the path deficit metric derived from the inverse dynamics velocity field to perform adaptive weighted inverse sampling, is presented as sufficient to recover high-fidelity semantics after channel-driven truncation. Diffusion models are known to be sensitive to the number and starting point of denoising steps; without explicit analysis or ablation showing that the metric reliably identifies and corrects deficient paths across variable truncation lengths induced by fast fading, the maintained-fidelity part of the tradeoff remains unproven.
minor comments (2)
  1. [Abstract] The abstract could briefly indicate the number of users, the specific channel model (e.g., Rayleigh or Rician parameters), and the diffusion model architecture employed, to give readers immediate context for the reported gains.
  2. [Notation and Definitions] Notation for the countdown timer and the path deficit metric should be introduced once with clear mathematical definitions and then used consistently; currently the transition from scheduling rule to receiver compensation is described only at a high level.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and will revise the manuscript to strengthen the presentation of our experimental results and the validation of the path compensation mechanism.

read point-by-point responses

  1. Referee: The abstract states that experimental evaluations demonstrate substantial end-to-end latency reduction while maintaining high-fidelity semantic reconstruction, yet no simulation setup, baseline algorithms, error bars, data exclusion criteria, or statistical significance tests are described. This information is load-bearing for the central performance claim and must be supplied with sufficient detail for independent verification.

    Authors: We agree that the current manuscript lacks sufficient detail on the experimental methodology to enable full reproducibility and independent verification of the latency-fidelity claims. In the revised version we will add a dedicated experimental setup subsection that specifies the simulation parameters (including fading channel models, SNR ranges, and diffusion model hyperparameters), the complete set of baseline algorithms (non-preemptive scheduling, standard semantic communication without truncation, and conventional preemptive schemes), the number of Monte Carlo runs with error bars, any data exclusion criteria, and the statistical tests (e.g., paired t-tests) used to confirm significance of the reported latency reductions and reconstruction quality metrics. revision: yes

  2. Referee: The receiver-side path compensation mechanism, which relies on the path deficit metric derived from the inverse dynamics velocity field to perform adaptive weighted inverse sampling, is presented as sufficient to recover high-fidelity semantics after channel-driven truncation. Diffusion models are known to be sensitive to the number and starting point of denoising steps; without explicit analysis or ablation showing that the metric reliably identifies and corrects deficient paths across variable truncation lengths induced by fast fading, the maintained-fidelity part of the tradeoff remains unproven.

    Authors: We acknowledge the referee's valid point regarding the known sensitivity of diffusion models to the number and starting point of denoising steps. While the manuscript formally defines the path deficit metric and the adaptive weighted inverse sampling procedure, it does not yet contain dedicated ablation studies across varying truncation lengths. In the revision we will add an ablation subsection that evaluates the path compensation mechanism under different truncation lengths corresponding to fast-fading realizations. This will include quantitative comparisons (with and without compensation) using standard semantic fidelity metrics, analysis of how the velocity-field-derived deficit identifies deficient paths, and illustrative examples of the resulting inverse sampling trajectories to demonstrate reliable recovery of high-fidelity semantics. revision: yes

Circularity Check

0 steps flagged

No circularity: framework defined by explicit mechanisms and validated experimentally

full rationale

The paper introduces a channel-aware preemptive scheduling framework (CAPS-TDPC) consisting of a countdown timer inversely proportional to instantaneous channel gain and a receiver-side path deficit metric derived from the inverse dynamics velocity field for adaptive weighted sampling. These are presented as design choices to address the conflict between diffusion iteration delays and fast fading, with performance claims resting on experimental evaluations rather than any closed-form derivation that reduces to its own inputs. No self-definitional equations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the abstract or described structure. The derivation chain is self-contained as an engineering proposal with independent empirical support.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; full details on parameters or assumptions are inaccessible. The countdown proportionality and path-deficit weighting may embed design choices that function as free parameters, but none are explicitly quantified here.

pith-pipeline@v0.9.0 · 5551 in / 1193 out tokens · 74676 ms · 2026-05-10T16:18:37.909255+00:00 · methodology

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Reference graph

Works this paper leans on

33 extracted references · 33 canonical work pages · 1 internal anchor

  1. [1]

    Breaking the interference and fading gridlock in backscatter communications: State- of-the-art, design challenges, and future directions,

    B. Gu, D. Li, H. Ding, G. Wang, and C. Tellambura, “Breaking the interference and fading gridlock in backscatter communications: State- of-the-art, design challenges, and future directions,”IEEE Commun. Surv. Tutorials, pp. 1–1, Jul. 2024

    work page 2024

  2. [2]

    Less data, more knowledge: Building next-generation semantic communica- tion networks,

    C. Chaccour, W. Saad, M. Debbah, Z. Han, and H. Vincent Poor, “Less data, more knowledge: Building next-generation semantic communica- tion networks,”IEEE Commun. Surv. Tutorials, vol. 27, no. 1, pp. 37–76, Feb. 2025

    work page 2025

  3. [3]

    From data mirror to smart copilot: A survey on nextg semantic communication for propelling digital twin world into cognitive stage,

    F. Zhu, J. Chen, J. Wen, Y . Yang, C. Yi, Y . Tie, P. Zhang, J. Cai, D. Niyato, and M. Guizani, “From data mirror to smart copilot: A survey on nextg semantic communication for propelling digital twin world into cognitive stage,”IEEE Commun. Surv. Tutorials, vol. 28, pp. 4915–4947, 2026

    work page 2026

  4. [4]

    Generative ai-enabled semantic communication: State-of-the-art, applications, and the way ahead,

    C. Liang and D. Li, “Generative ai-enabled semantic communication: State-of-the-art, applications, and the way ahead,”IEEE Commun. Surv. Tutorials, vol. 28, pp. 3976–4015, 2026

    work page 2026

  5. [5]

    Online energy efficient multimodal probabilistic semantic communication,

    J. Dai, J. Li, Z. Zhao, Z. Yang, J. Ye, Q. Yang, C. Huang, and Z. Zhang, “Online energy efficient multimodal probabilistic semantic communication,”IEEE Internet Things J., vol. 13, no. 1, pp. 69–86, Jan. 2026

    work page 2026

  6. [6]

    A contemporary survey on semantic communications: Theory of mind, generative ai, and deep joint source-channel coding,

    L. X. Nguyen, A. D. Raha, P. S. Aung, D. Niyato, Z. Han, and C. S. Hong, “A contemporary survey on semantic communications: Theory of mind, generative ai, and deep joint source-channel coding,”IEEE Commun. Surv. Tutorials, vol. 28, pp. 2377–2417, 2026

    work page 2026

  7. [7]

    Resource allocation driven by large models in future semantic-aware networks,

    H. Zhang, J. Ni, Z. Wu, X. Liu, and V . C. M. Leung, “Resource allocation driven by large models in future semantic-aware networks,” IEEE Wireless Commun., vol. 32, no. 4, pp. 116–122, Aug. 2025

    work page 2025

  8. [8]

    Joint source–channel noise adding with adaptive denoising for diffusion-based semantic communications,

    C. Liang and D. Li, “Joint source–channel noise adding with adaptive denoising for diffusion-based semantic communications,”IEEE Internet Things J., vol. 12, no. 21, pp. 45 909–45 912, Nov. 2025

    work page 2025

  9. [9]

    Semantic successive refinement: A generative ai-aided semantic communication framework,

    K. Zhang, L. Li, W. Lin, Y . Yan, R. Li, W. Cheng, and Z. Han, “Semantic successive refinement: A generative ai-aided semantic communication framework,”IEEE Trans. Cognit. Commun. Networking, vol. 11, no. 2, pp. 687–699, Apr. 2025

    work page 2025

  10. [10]

    Channel calibration for cell-free massive mimo systems using diffusion model,

    S. Xu, Z. Zhang, C. Li, X. Chen, L. Yang, and A. Nallanathan, “Channel calibration for cell-free massive mimo systems using diffusion model,” IEEE Trans. Wirel. Commun., vol. 25, pp. 8859–8873, 2026. 12

    work page 2026

  11. [11]

    A unified multi- task semantic communication system for multimodal data,

    G. Zhang, Q. Hu, Z. Qin, Y . Cai, G. Yu, and X. Tao, “A unified multi- task semantic communication system for multimodal data,”IEEE Trans. Commun., vol. 72, no. 7, pp. 4101–4116, Jul. 2024

    work page 2024

  12. [12]

    A novel lightweight joint source- channel coding design in semantic communications,

    X. Yu, D. Li, N. Zhang, and X. Shen, “A novel lightweight joint source- channel coding design in semantic communications,”IEEE Internet Things J., vol. 12, no. 11, pp. 18 447–18 450, Jun. 2025

    work page 2025

  13. [13]

    Semantic- importance-aware communication over mimo fading channels,

    H. Liang, C. Dong, W. An, Z. Bao, X. Xu, and R. Meng, “Semantic- importance-aware communication over mimo fading channels,”IEEE Internet Things J., vol. 12, no. 18, pp. 38 540–38 555, Sep. 2025

    work page 2025

  14. [14]

    Semantic communications for digital signals via carrier images,

    Z. Yan and D. Li, “Semantic communications for digital signals via carrier images,”IEEE Wireless Commun. Lett., vol. 14, no. 6, pp. 1816– 1820, Jun. 2025

    work page 2025

  15. [15]

    Latent diffusion model-enabled low-latency semantic communication in the presence of semantic ambiguities and wireless channel noises,

    J. Pei, C. Feng, P. Wang, H. Tabassum, and D. Shi, “Latent diffusion model-enabled low-latency semantic communication in the presence of semantic ambiguities and wireless channel noises,”IEEE Trans. Wireless Commun., vol. 24, no. 5, pp. 4055–4072, May 2025

    work page 2025

  16. [16]

    A lightweight-to-diffusion framework for semantic image communica- tions,

    T. Huynh-The, T. V . Nguyen, P. L. V o, and H.-T. Nguyen, “A lightweight-to-diffusion framework for semantic image communica- tions,”ICT Express, vol. 12, no. 1, pp. 175–179, Feb. 2026

    work page 2026

  17. [17]

    Image generation with supervised selection based on multimodal features for semantic communications,

    C. Liang and D. Li, “Image generation with supervised selection based on multimodal features for semantic communications,”IEEE Trans. Commun., vol. 73, no. 12, pp. 14 469–14 485, Dec. 2025

    work page 2025

  18. [18]

    Optimizing resource allocation for multi-modal semantic communication in mobile aigc networks: A diffusion-based game approach,

    J. Liu, M. Xiao, J. Wen, J. Kang, R. Zhang, T. Zhang, D. Niyato, W. Zhang, and Y . Liu, “Optimizing resource allocation for multi-modal semantic communication in mobile aigc networks: A diffusion-based game approach,”IEEE Trans. Cognit. Commun. Networking, vol. 11, no. 5, pp. 3346–3360, Oct. 2025

    work page 2025

  19. [19]

    Communicate less, synthesize the rest: Latency-aware intent-based gen- erative semantic multicasting with diffusion models,

    X. Liu, M. B. Mashhadi, L. Qiao, Y . Ma, R. Tafazolli, and M. Bennis, “Communicate less, synthesize the rest: Latency-aware intent-based gen- erative semantic multicasting with diffusion models,”IEEE Trans. Veh. Technol., early access, Feb. 02, 2026, doi: 10.1109/TVT.2026.3660013

    work page doi:10.1109/tvt.2026.3660013 2026

  20. [20]

    Lightweight diffusion models for resource-constrained semantic com- munication,

    E. Grassucci, G. Pignata, G. Cicchetti, and D. Comminiello, “Lightweight diffusion models for resource-constrained semantic com- munication,”IEEE Wireless Commun. Lett., vol. 14, no. 9, pp. 2743– 2747, Sep. 2025

    work page 2025

  21. [21]

    Multi-user semantic fusion for semantic communications over degraded broadcast channels,

    W. Tong, C. Zhiyong, T. Meixia, X. Bin, and Z. Wenjun, “Multi-user semantic fusion for semantic communications over degraded broadcast channels,”China Commun., vol. 21, no. 10, pp. 1–15, Oct. 2024

    work page 2024

  22. [22]

    Semantic security-aware multi-user resource allo- cation: A novel meaning-first scheduling,

    J. Lang and M. Ji, “Semantic security-aware multi-user resource allo- cation: A novel meaning-first scheduling,”Phys. Commun., vol. 74, p. 102928, Feb. 2026

    work page 2026

  23. [23]

    Multi-user generative semantic communication with intent- aware semantic-splitting multiple access,

    J. Lu, W. Yang, Z. Xiong, R. Tafazolli, T. Q. S. Quek, M. Debbah, and D. I. Kim, “Multi-user generative semantic communication with intent- aware semantic-splitting multiple access,” 2025,arXiv:2507.01333

    work page arXiv 2025

  24. [24]

    Semantic prior aided channel-adaptive equalizing and de-noising se- mantic communication system with latent diffusion model,

    B. Xu, S. Han, X. Xu, W. Li, R. Meng, C. Dong, and P. Zhang, “Semantic prior aided channel-adaptive equalizing and de-noising se- mantic communication system with latent diffusion model,”IEEE Trans. Wireless Commun., vol. 24, no. 6, pp. 4614–4630, Jun. 2025

    work page 2025

  25. [25]

    Adaptive- awareness for ris-enhanced semantic communications (risemcom) in dynamic random environment,

    Z. Zhu, X. Liang, Z. Chu, G. Sun, D. Mi, and M. Debbah, “Adaptive- awareness for ris-enhanced semantic communications (risemcom) in dynamic random environment,”IEEE Trans. Mob. Comput., early access, Feb. 24, 2026, 10.1109/TMC.2026.3667836

    work page doi:10.1109/tmc.2026.3667836 2026

  26. [26]

    Wireless vision-centered semantic communica- tion for smart city environment: Pretrained network and quantization,

    Y . Gong, Z. Chu, Z. Zhu, P. Xiao, M. Zeng, Y . Wang, H. M. Pandey, and J. Hou, “Wireless vision-centered semantic communica- tion for smart city environment: Pretrained network and quantization,” IEEE Trans. Consum. Electron., early access, Jan. 21, 2026, doi: 10.1109/TCE.2026.3656549

    work page doi:10.1109/tce.2026.3656549 2026

  27. [27]

    Joint mixed semantic communica- tion and computing offloading framework for mobile edge computing,

    Z. Shan, H. Zhang, Z. Fu, and P. Xin, “Joint mixed semantic communica- tion and computing offloading framework for mobile edge computing,” inProc. IEEE/CIC International Conference on Communications in China (ICCC), 2025, pp. 1–6

    work page 2025

  28. [28]

    Semcom-optima: Empirically-driven optimization of semantic image transmission across heterogeneous edge–cloud systems,

    H. Saadat, A. Albaseer, M. Abdallah, A. Mohamed, and A. Erbad, “Semcom-optima: Empirically-driven optimization of semantic image transmission across heterogeneous edge–cloud systems,”IEEE Open J. Comput. Soc., vol. 7, pp. 93–104, 2026

    work page 2026

  29. [29]

    Flow Matching for Generative Modeling

    Y . Lipman, R. T. Chen, H. Ben-Hamu, M. Nickel, and M. Le, “Flow matching for generative modeling,” 2022,arXiv:2210.02747

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  30. [30]

    Latency-aware generative semantic communications with pre-trained diffusion models,

    L. Qiao, M. B. Mashhadi, Z. Gao, C. H. Foh, P. Xiao, and M. Bennis, “Latency-aware generative semantic communications with pre-trained diffusion models,”IEEE Wireless Commun. Lett., vol. 13, no. 10, pp. 2652–2656, Oct. 2024

    work page 2024

  31. [31]

    Deep joint source- channel coding for wireless image transmission,

    E. Bourtsoulatze, D. Burth Kurka, and D. G ¨und¨uz, “Deep joint source- channel coding for wireless image transmission,”IEEE Trans. Cognit. Commun. Networking, vol. 5, no. 3, pp. 567–579, Sep. 2019

    work page 2019

  32. [32]

    Cddm: Channel denoising diffusion models for wireless semantic communications,

    T. Wu, Z. Chen, D. He, L. Qian, Y . Xu, M. Tao, and W. Zhang, “Cddm: Channel denoising diffusion models for wireless semantic communications,”IEEE Trans. Wirel. Commun., vol. 23, no. 9, pp. 11 168–11 183, Sep. 2024

    work page 2024

  33. [33]

    Witt: A wireless image transmission transformer for semantic communications,

    K. Yang, S. Wang, J. Dai, K. Tan, K. Niu, and P. Zhang, “Witt: A wireless image transmission transformer for semantic communications,” inProc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), 2023, pp. 1–5

    work page 2023