Pith. sign in

REVIEW 4 minor 26 references

In single-source ISAC systems the optimal AoI policy has an ordered threshold structure on its two-dimensional state.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

T0 review · grok-4.3

2026-06-30 11:51 UTC pith:2MKRA6NW

load-bearing objection The paper proves an ordered threshold structure for the two-dimensional AoI MDP in a three-mode ISAC channel and supplies an explicit truncation error bound; the multi-source Whittle extension is more routine.

arxiv 2605.24714 v1 pith:2MKRA6NW submitted 2026-05-23 cs.IT cs.NImath.IT

Age of Information Optimization for Status Updates in Integrated Sensing and Communication Systems

Marco Zanni , Mohamad Assaad , Touraj Soleymani This is my paper
classification cs.IT cs.NImath.IT
keywords age of informationintegrated sensing and communicationMarkov decision processthreshold policyrestless multi-armed banditWhittle index
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper studies how a base station should choose among sensing, communication, and joint modes to keep status information fresh at a monitor while paying different costs for each choice. It models the single-source case as an infinite-horizon discounted MDP whose state is the pair of ages of information at the monitor and at the sensor. The central result is a proof that the optimal stationary policy is monotone in a specific ordered-threshold sense across this two-dimensional state space. The authors also derive an analytic truncation of the state space that keeps the optimality gap below any prescribed level. For multiple sources the scheduling task is recast as a restless bandit and solved with both exact and approximate Whittle-index policies.

Core claim

For the single source scenario, we formulate the problem as a Markov decision process with a two-dimensional AoI state and prove that the optimal stationary policy admits an ordered threshold structure in the AoI state space. Since the AoI evolves over an infinite space, we truncate the state space to reduce complexity and rigorously bound the resulting error. The analysis analytically determines the truncation size needed to keep the error below a given threshold. For the multi-source scenario, we formulate the scheduling problem as a restless multi-armed bandit and develop both a Whittle index policy and an approximate Whittle index policy.

What carries the argument

The two-dimensional AoI Markov decision process whose optimal stationary policy is proved to possess an ordered threshold structure.

Load-bearing premise

The system can be modeled as a discrete-time process with exactly three mutually exclusive modes whose success probabilities and costs are fixed constants independent of the current ages.

What would settle it

A value-iteration computation on a sufficiently large finite truncation that produces an optimal policy whose action regions violate the claimed ordered threshold ordering for at least one pair of AoI values.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • The optimal policy can be computed by searching only over candidate threshold pairs rather than over the full policy space.
  • The truncation size required for any target error can be calculated in closed form before running the algorithm.
  • In the multi-source case the Whittle-index policy is optimal when indexability holds and remains competitive when it does not.
  • The same structural result immediately yields a simple online scheduler once the thresholds are tabulated.

Where Pith is reading between the lines

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

  • The threshold structure may permit a low-memory lookup-table implementation on resource-limited base stations.
  • The same MDP formulation could be reused to study continuous-time or energy-harvesting variants by changing only the transition probabilities.
  • If the three-mode assumption is relaxed to allow mode-dependent reliability that varies with current AoI, the threshold property would have to be re-proved.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

0 major / 4 minor

Summary. The paper studies AoI optimization in an ISAC system with three discrete-time modes (sensing, communication, joint) having fixed success probabilities and costs. For the single-source case it formulates a discounted infinite-horizon MDP whose state is the pair of AoIs, proves that the optimal stationary policy has an ordered threshold structure, and supplies a state-space truncation together with an explicit error bound that determines the required truncation size. For the multi-source case it casts the problem as a restless multi-armed bandit, derives both a Whittle-index policy (when indexability holds) and an approximate Whittle-index policy (when it does not), and presents numerical illustrations of the threshold structure and policy performance.

Significance. If the threshold-structure proof and the truncation error bound hold, the work supplies a concrete structural result and a computationally tractable approximation for an infinite-state MDP arising in ISAC, which is a useful addition to the AoI literature. The explicit analytic determination of truncation size and the extension of Whittle indexing to the non-indexable regime are strengths that enhance practical applicability. These elements, together with the standard but carefully applied MDP and restless-bandit machinery, give the manuscript a solid technical foundation.

minor comments (4)
  1. The precise definition of the 'ordered threshold structure' (e.g., the partial order on the two-dimensional AoI state space and the monotonicity direction of the switching curve) should be stated explicitly in the single-source formulation section rather than left implicit in the proof.
  2. The transition probabilities and immediate costs for each of the three modes are described qualitatively; writing the explicit four-tuple (p_s, c_s, p_c, c_c, p_j, c_j) and the resulting AoI update rules in a single displayed equation would improve verifiability of the MDP.
  3. In the multi-source section the condition that distinguishes the indexable regime from the non-indexable regime is stated but not accompanied by a simple, checkable criterion on the per-source parameters; adding such a criterion would clarify when each policy is applicable.
  4. Numerical figures would benefit from error bars or multiple random seeds to confirm that the reported performance gap between the approximate Whittle policy and the exact Whittle policy is statistically stable.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary, significance assessment, and recommendation of minor revision. No major comments appear in the report, so we have no specific points to address point-by-point. We will incorporate any minor editorial suggestions in the revised version.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The derivation applies standard discounted infinite-horizon MDP value iteration and monotonicity/submodularity arguments to establish the ordered threshold structure for the two-dimensional AoI state; these arguments are external to the paper and do not reduce to any fitted parameter or self-citation. The truncation error bound is derived from the same contraction mapping and is independent of the policy structure. The restless-bandit formulation likewise invokes the standard Whittle indexability condition and index policy without any self-referential reduction. No step matches any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on standard MDP and restless-bandit assumptions plus the modeling choice of three discrete modes; no free parameters, invented entities, or non-standard axioms are introduced in the abstract.

axioms (2)
  • domain assumption The joint sensing-communication system can be represented as a discrete-time MDP whose state is fully described by a two-dimensional AoI vector and whose actions are the three modes with fixed but distinct success probabilities and costs.
    Invoked in the single-source formulation paragraph of the abstract.
  • domain assumption The infinite-horizon discounted cost admits an optimal stationary policy whose structure can be characterized by ordered thresholds in the AoI plane.
    Central to the proof claim in the abstract.

pith-pipeline@v0.9.1-grok · 5803 in / 1472 out tokens · 30656 ms · 2026-06-30T11:51:34.716174+00:00 · methodology

0 comments
read the original abstract

In this paper, we study age of information (AoI) optimization for status updating in an integrated sensing and communication (ISAC) system. We consider a discrete-time architecture in which a base station interacts with a physical environment and a remote monitor, and at each time slot can operate in one of three modes: sensing, communication, or joint sensing and communication. Each mode is unreliable and incurs a different operational cost. The objective is to minimize a discounted infinite-horizon cost that combines the AoI at the monitor with action-dependent sensing and communication costs. For the single source scenario, we formulate the problem as a Markov decision process with a two-dimensional AoI state and prove that the optimal stationary policy admits an ordered threshold structure in the AoI state space. Since the AoI evolves over an infinite space, we truncate the state space to reduce complexity and rigorously bound the resulting error. The analysis analytically determines the truncation size needed to keep the error below a given threshold. For the multi-source scenario, we formulate the scheduling problem as a restless multi-armed bandit. We develop both a Whittle index policy and an approximate Whittle index policy for scheduling under two different regimes, one where indexability is guaranteed, and one where it is not. Numerical results illustrate the structure of the optimal policy in the single-source case and show that the proposed approximate Whittle index policy performs comparably to the Whittle index policy in the indexable regime, while remaining effective beyond it.

Figures

Figures reproduced from arXiv: 2605.24714 by Marco Zanni, Mohamad Assaad, Touraj Soleymani.

Figure 1
Figure 1. Figure 1: Representative example of an ISAC architecture for remotely [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Value function as a function of the monitor and base station [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Optimal ISAC action map as a function of the monitor and base [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Average discounted cost per source J with varying N in the indexable regime: comparison between WIP, AWIP, the random policy, and the greedy policy. 99% confidence intervals are also displayed [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Average discounted cost per source J with varying N when the sufficient condition for indexability is violated: comparison between AWIP, the random policy, and the greedy policy. 99% confidence intervals are also displayed. freshness objectives. We formulated the single process-monitor problem as a discounted infinite-horizon Markov decision process, and established that the optimal stationary policy admit… view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

26 extracted references · 26 canonical work pages · 2 internal anchors

  1. [1]

    Real-time status: How often should one update?

    S. Kaul, R. D. Yates, and M. Gruteser, “Real-time status: How often should one update?” inProceedings of IEEE INFOCOM, 2012, pp. 2731–2735

    work page 2012

  2. [2]

    Age of information: An introduction and survey,

    R. D. Yates, Y . Sun, D. R. B. III, S. K. Kaul, E. Modiano, and S. Ulukus, “Age of information: An introduction and survey,”IEEE Journal on Selected Areas in Communications, vol. 39, no. 5, pp. 1183–1210, 2021

    work page 2021

  3. [3]

    Update or wait: How to keep your data fresh,

    Y . Sun, E. Uysal-Biyikoglu, R. D. Yates, C. E. Koksal, and N. B. Shroff, “Update or wait: How to keep your data fresh,”IEEE Transactions on Information Theory, vol. 63, no. 11, pp. 7492–7508, 2017

    work page 2017

  4. [4]

    On the global optimality of Whittle’s index policy for minimizing the age of information,

    S. Kriouile, M. Assaad, and A. Maatouk, “On the global optimality of Whittle’s index policy for minimizing the age of information,”IEEE Transactions on Information Theory, vol. 68, no. 1, pp. 572–600, 2022

    work page 2022

  5. [5]

    The age of incorrect in- formation: An enabler of semantics-empowered communication,

    A. Maatouk, M. Assaad, and A. Ephremides, “The age of incorrect in- formation: An enabler of semantics-empowered communication,”IEEE Transactions on Wireless Communications, vol. 22, no. 4, pp. 2621– 2635, 2023

    work page 2023

  6. [6]

    Value of information in feedback control: Quantification,

    T. Soleymani, J. S. Baras, and S. Hirche, “Value of information in feedback control: Quantification,”IEEE Transactions on Automatic Control, vol. 67, no. 7, pp. 3730–3737, 2022

    work page 2022

  7. [7]

    Value of information in feedback control: Global optimality,

    T. Soleymani, J. S. Baras, S. Hirche, and K. H. Johansson, “Value of information in feedback control: Global optimality,”IEEE Transactions on Automatic Control, vol. 68, no. 6, pp. 3641–3647, 2023

    work page 2023

  8. [8]

    Scheduling policies for minimizing age of information in broadcast wireless networks,

    I. Kadota, A. Sinha, E. Uysal-Biyikoglu, R. Singh, and E. H. Modiano, “Scheduling policies for minimizing age of information in broadcast wireless networks,”IEEE/ACM Transactions on Networking, vol. 26, no. 6, pp. 2637–2650, 2018

    work page 2018

  9. [9]

    Closed-form Whittle’s index-enabled random access for timely status update,

    J. Sun, Z. Jiang, B. Krishnamachari, S. Zhou, and Z. Niu, “Closed-form Whittle’s index-enabled random access for timely status update,”IEEE Transactions on Communications, vol. 68, no. 3, pp. 1538–1551, 2020

    work page 2020

  10. [10]

    A Whittle index approach to mini- mizing functions of age of information,

    V . Tripathi and E. H. Modiano, “A Whittle index approach to mini- mizing functions of age of information,”IEEE/ACM Transactions on Networking, vol. 32, no. 6, pp. 5144–5158, 2024

    work page 2024

  11. [11]

    An easier-to-verify sufficient condition for Whittle indexability and application to AoI minimization,

    S. Zhou and X. Lin, “An easier-to-verify sufficient condition for Whittle indexability and application to AoI minimization,” inIEEE INFOCOM 2024 – IEEE Conference on Computer Communications, 2024, pp. 1741–1750

    work page 2024

  12. [12]

    Age-of- information-aware federated learning,

    Y . Xu, M.-J. Xiao, C. Wu, J. Wu, J.-R. Zhou, and H. Sun, “Age-of- information-aware federated learning,”Journal of Computer Science and Technology, vol. 39, no. 3, pp. 637–653, 2024

    work page 2024

  13. [13]

    Optimizing AoI at query in multiuser wireless uplink networks: A Whittle index approach,

    J. Liu and H. Chen, “Optimizing AoI at query in multiuser wireless uplink networks: A Whittle index approach,”IEEE Transactions on Communications, vol. 73, no. 11, pp. 10 318–10 329, 2025

    work page 2025

  14. [14]

    On an index policy for restless bandits,

    R. R. Weber and G. Weiss, “On an index policy for restless bandits,” Journal of Applied Probability, vol. 27, no. 3, pp. 637–648, 1990

    work page 1990

  15. [15]

    Asymptot- ically optimal pilot allocation over markovian fading channels,

    M. Larra ˜naga, M. Assaad, A. Destounis, and G. S. Paschos, “Asymptot- ically optimal pilot allocation over markovian fading channels,”IEEE Transactions on Information Theory, vol. 64, no. 7, pp. 5395–5418, 2017

    work page 2017

  16. [16]

    Joint radar and communication design: Applications, state-of-the-art, and the road ahead,

    F. Liu, C. Masouros, A. P. Petropulu, H. Griffiths, and L. Hanzo, “Joint radar and communication design: Applications, state-of-the-art, and the road ahead,”IEEE Transactions on Communications, vol. 68, no. 6, pp. 3834–3862, 2020

    work page 2020

  17. [17]

    Radio resource management in joint radar and communication: A comprehen- sive survey,

    N. C. Luong, X. Lu, D. T. Hoang, D. Niyato, and D. I. Kim, “Radio resource management in joint radar and communication: A comprehen- sive survey,”IEEE Communications Surveys & Tutorials, vol. 23, no. 2, pp. 780–814, 2021

    work page 2021

  18. [18]

    Intelligent integrated sensing and communication: A survey,

    J. Zhang, W. Lu, C. Xing, N. Zhao, N. Al-Dhahir, G. K. Karagiannidis, and X. Yang, “Intelligent integrated sensing and communication: A survey,”Science China Information Sciences, vol. 68, no. 3, p. 131301, 2025

    work page 2025

  19. [19]

    A survey on integrated sensing, communication, and computation,

    D. Wen, Y . Zhou, X. Li, Y . Shi, K. Huang, and K. B. Letaief, “A survey on integrated sensing, communication, and computation,”IEEE Communications Surveys & Tutorials, vol. 27, no. 5, pp. 3058–3098, 2025

    work page 2025

  20. [20]

    Optimizing multi-UA V multi-user system through integrated sensing and communi- cation for age of information (AoI) analysis,

    Y . Zhou, A. A. Khuwaja, X. Li, N. Zhao, and Y . Chen, “Optimizing multi-UA V multi-user system through integrated sensing and communi- cation for age of information (AoI) analysis,”IEEE Open Journal of the Communications Society, vol. 5, pp. 6918–6931, 2024

    work page 2024

  21. [21]

    Age of Information minimization in UA V-enabled integrated sensing and communication systems,

    Y . Bai, Y . Zhang, B. Xie, Z. Chang, Y . Zhang, R. J ¨antti, and Z. Han, “Age of Information minimization in UA V-enabled integrated sensing and communication systems,”arXiv preprint arXiv:2507.14299, 2025

    work page arXiv 2025

  22. [22]

    Joint sensing and age of information optimization for energy constrained UA V-assisted integrated sensing, calculation, and communication,

    Z. Liu, X. Liu, W. Yang, and X. Zhang, “Joint sensing and age of information optimization for energy constrained UA V-assisted integrated sensing, calculation, and communication,”IEEE Transactions on Wire- less Communications, vol. 24, no. 5, pp. 4440–4453, 2025

    work page 2025

  23. [23]

    AoI minimization for air- ground integrated sensing and communication networks with jamming attack,

    H. Mei, H. Zhang, X. Zhou, and J. Wang, “AoI minimization for air- ground integrated sensing and communication networks with jamming attack,”IEEE Transactions on Vehicular Technology, vol. 74, no. 8, pp. 12 776–12 790, 2025

    work page 2025

  24. [24]

    Heterogeneous Mixture-of-Experts for Energy-Efficient Multimodal ISAC in Highly Mobile Networks

    W. Fan, N. Wei, R. Xi, A. Bazzi, Y . Xiu, C. Assi, J. Dong, and J. Jin, “Heterogeneous mixture-of-experts for energy-efficient multimodal ISAC in highly mobile networks,”arXiv preprint arXiv:2604.06697, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  25. [25]

    Status updating via integrated sensing and communication: freshness optimisation,

    T. Soleymani, M. Assaad, and J. S. Baras, “Status updating via integrated sensing and communication: freshness optimisation,”arXiv preprint arXiv:2601.22901, 2026

    work page internal anchor Pith review arXiv 2026

  26. [26]

    Value function based reinforcement learning in changing markovian environments,

    B. C. Cs ´aji and L. Monostori, “Value function based reinforcement learning in changing markovian environments,”Journal of Machine Learning Research, vol. 9, no. 54, pp. 1679–1709, 2008. [Online]. Available: http://jmlr.org/papers/v9/csaji08a.html 16 APPENDIXA PROOF OFLEMMA4 LetV (0) ≡0and defineV (n+1) =T V (n) forn≥0. For eachn≥0, set Ln := inf αm≥αb≥1...

    work page 2008