Opportunistic Scheduling as Restless Bandits
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Abstract (arXiv)
In this paper we consider energy efficient scheduling in a multiuser setting where each user has a finite sized queue and there is a cost associated with holding packets (jobs) in each queue (modeling the delay constraints). The packets of each user need to be sent over a common channel. The channel qualities seen by the users are time-varying and differ across users; also, the cost incurred, i.e., energy consumed, in packet transmission is a function of the channel quality. We pose the problem as an average cost Markov Decision Problem, and prove that this problem is Whittle Indexable. Based on this result, we propose an algorithm in which the Whittle index of each user is computed and the user who has the lowest value is selected for transmission. We evaluate the performance of this algorithm via simulations and show that it achieves a lower average cost than the Maximum Weight Scheduling and Weighted Fair Scheduling strategies.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jun 2017.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 5; 2025: 5; 2026: 1.
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- 2022Optimistic Whittle Index Policy: Online Learning for Restless Banditscited by 18
- 2022On learning Whittle index policy for restless bandits with scalable regretcited by 9
- 2024Global Rewards in Restless Multi-Armed Banditscited by 6
- 2024Faster Q-Learning Algorithms for Restless Banditscited by 2
- 2023Indexability of Finite State Restless Multi-Armed Bandit and Rollout Policycited by 1
- 2024Deep Index Policy for Multi-Resource Restless Matching Bandit and Its Application in Multi-Channel Schedulingcited by 1
- 2024Whittle Index Learning Algorithms for Restless Bandits with Constant Stepsizescited by 1
- 2025Online Learning of Whittle Indices for Restless Bandits with Non-Stationary Transition Kernelscited by 1
- 2025Model Predictive Control is almost Optimal for Heterogeneous Restless Multi-armed Banditscited by 1
- 2024Risk-Aware Decision Making in Restless Bandits: Theory and Algorithms for Planning and Learningcited by 0
- 2025From Restless to Contextual: A Thresholding Bandit Reformulation For Finite-horizon Improvementcited by 0
- 2025Neural Index Policies for Restless Multi-Action Bandits with Heterogeneous Budgetscited by 0
- 2026Restless Bandits with Individual Penalty Constraints: Near-Optimal Indices and Deep Reinforcement Learning5 Apr 2026
- 2025Model-Based Learning of Whittle indices25 Nov 2025
- 2025Model Predictive Control is almost Optimal for Heterogeneous Restless Multi-armed Bandits11 Nov 2025
- 2025Neural Index Policies for Restless Multi-Action Bandits with Heterogeneous Budgets24 Oct 2025
- 2025Online Learning of Whittle Indices for Restless Bandits with Non-Stationary Transition Kernels22 Jun 2025
- 2025From Restless to Contextual: A Thresholding Bandit Reformulation For Finite-horizon Improvement7 Feb 2025
- 2024Risk-Aware Decision Making in Restless Bandits: Theory and Algorithms for Planning and Learning30 Oct 2024
- 2024Faster Q-Learning Algorithms for Restless Bandits6 Sep 2024
- 2024Whittle Index Learning Algorithms for Restless Bandits with Constant Stepsizes6 Sep 2024
- 2024Deep Index Policy for Multi-Resource Restless Matching Bandit and Its Application in Multi-Channel Scheduling13 Aug 2024
- 2024Global Rewards in Restless Multi-Armed Bandits2 Jun 2024
- 2023Indexability of Finite State Restless Multi-Armed Bandit and Rollout Policy30 Apr 2023
- 2022Optimistic Whittle Index Policy: Online Learning for Restless Banditscited by 18
- 2022On learning Whittle index policy for restless bandits with scalable regretcited by 9
- 2024Global Rewards in Restless Multi-Armed Banditscited by 6
- 2024Faster Q-Learning Algorithms for Restless Banditscited by 2
- 2023Indexability of Finite State Restless Multi-Armed Bandit and Rollout Policycited by 1
- 2024Deep Index Policy for Multi-Resource Restless Matching Bandit and Its Application in Multi-Channel Schedulingcited by 1
- 2024Whittle Index Learning Algorithms for Restless Bandits with Constant Stepsizescited by 1
- 2025Online Learning of Whittle Indices for Restless Bandits with Non-Stationary Transition Kernelscited by 1
- 2025Model Predictive Control is almost Optimal for Heterogeneous Restless Multi-armed Banditscited by 1
- 2024Risk-Aware Decision Making in Restless Bandits: Theory and Algorithms for Planning and Learningcited by 0
- 2025From Restless to Contextual: A Thresholding Bandit Reformulation For Finite-horizon Improvementcited by 0
- 2025Neural Index Policies for Restless Multi-Action Bandits with Heterogeneous Budgetscited by 0
12 most cited of 14 All 14 (JSON)
- 2026Restless Bandits with Individual Penalty Constraints: Near-Optimal Indices and Deep Reinforcement Learning5 Apr 2026
- 2025Model-Based Learning of Whittle indices25 Nov 2025
- 2025Model Predictive Control is almost Optimal for Heterogeneous Restless Multi-armed Bandits11 Nov 2025
- 2025Neural Index Policies for Restless Multi-Action Bandits with Heterogeneous Budgets24 Oct 2025
- 2025Online Learning of Whittle Indices for Restless Bandits with Non-Stationary Transition Kernels22 Jun 2025
- 2025From Restless to Contextual: A Thresholding Bandit Reformulation For Finite-horizon Improvement7 Feb 2025
- 2024Risk-Aware Decision Making in Restless Bandits: Theory and Algorithms for Planning and Learning30 Oct 2024
- 2024Faster Q-Learning Algorithms for Restless Bandits6 Sep 2024
- 2024Whittle Index Learning Algorithms for Restless Bandits with Constant Stepsizes6 Sep 2024
- 2024Deep Index Policy for Multi-Resource Restless Matching Bandit and Its Application in Multi-Channel Scheduling13 Aug 2024
- 2024Global Rewards in Restless Multi-Armed Bandits2 Jun 2024
- 2023Indexability of Finite State Restless Multi-Armed Bandit and Rollout Policy30 Apr 2023
12 newest of 14 All 14 (JSON)