mlsanityData to 13 Oct 2026

A Whittle Index Approach to Minimizing Functions of Age of Information

arXiv 1908.10438 ↗First posted 27 Aug 2019cs.IT cs.NI math.ITcited by 20

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Abstract (arXiv)

We consider a setting where multiple active sources send real-time updates over a single-hop wireless broadcast network to a monitoring station. Our goal is to design a scheduling policy that minimizes the time-average of general non-decreasing cost functions of Age of Information. We use a Whittle index based approach to find low complexity scheduling policies that have good performance. We prove that for a system with two sources, having possibly different cost functions and reliable channels, the Whittle index policy is exactly optimal. We derive structural properties of an optimal policy, that suggest that the performance of the Whittle index policy may be close to optimal in general. These results might also be of independent interest in the study of restless multi-armed bandit problems with similar underlying structure. We further establish that minimizing monitoring error for linear time-invariant systems and symmetric Markov chains is equivalent to minimizing appropriately chosen monotone functions of Age of Information. Finally, we provide simulations comparing the Whittle index policy with optimal scheduling policies found using dynamic programming, which support our results.

Key facts. Cited by 20 papers in the map (data through 13 Oct 2026). First posted on arXiv 27 Aug 2019.

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Citing papers per year: 2021: 2; 2022: 2; 2023: 2; 2024: 6; 2025: 4; 2026: 4.

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  1. 2021NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RLcited by 27
  2. 2022Indexability is Not Enough for Whittle: Improved, Near-Optimal Algorithms for Restless Banditscited by 10
  3. 2021An Online Learning Approach to Optimizing Time-Varying Costs of AoIcited by 5
  4. 2024Timely Communications for Remote Inferencecited by 5
  5. 2024The Bandit Whisperer: Communication Learning for Restless Banditscited by 5
  6. 2022How Does Data Freshness Affect Real-time Supervised Learning?cited by 3
  7. 2024Lagrangian Index Policy for Restless Bandits with Average Rewardcited by 3
  8. 2024Deep Index Policy for Multi-Resource Restless Matching Bandit and Its Application in Multi-Channel Schedulingcited by 2
  9. 2024Tabular and Deep Reinforcement Learning for Gittins Indexcited by 1
  10. 2024GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Banditscited by 1
  11. 2025Online Learning of Whittle Indices for Restless Bandits with Non-Stationary Transition Kernelscited by 1
  12. 2025Multimodal Remote Inferencecited by 1
8 more
  1. 2023Minimizing Age of Information for Mobile Edge Computing Systems: A Nested Index Approachcited by 0
  2. 2023Learning-based Scheduling for Information Accuracy and Freshness in Wireless Networkscited by 0
  3. 2025On the Regularity and Fairness of Combinatorial Multi-Armed Banditcited by 0
  4. 2025Online Learning for Optimizing AoI-Energy Tradeoff under Unknown Channel Statisticscited by 0
  5. 2026Adaptive Requesting in Decentralized Edge Networks via Non-Stationary Banditscited by 0
  6. 2026Real-Time Hard Peak Age-of-Information Safety with No-Regret Learningcited by 0
  7. 2026Threshold Structure of Optimal Policies in Restart POMDPscited by 0
  8. 2026Learning When to Update: A Near-Optimal Timing Bandit Approachcited by 0