User Association in Dense mmWave Networks as Restless Bandits
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Abstract (arXiv)
We study the problem of user association, i.e., determining which base station (BS) a user should associate with, in a dense millimeter wave (mmWave) network. In our system model, in each time slot, a user arrives with some probability in a region with a relatively small geographical area served by a dense mmWave network. Our goal is to devise an association policy under which, in each time slot in which a user arrives, it is assigned to exactly one BS so as to minimize the weighted average amount of time that users spend in the system. The above problem is a restless multi-armed bandit problem and is provably hard to solve. We prove that the problem is Whittle indexable, and based on this result, propose an association policy under which an arriving user is associated with the BS having the smallest Whittle index. Using simulations, we show that our proposed policy outperforms several user association policies proposed in prior work.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jul 2021.
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Citing papers per year: 2022: 3; 2023: 0; 2024: 1; 2025: 0; 2026: 1.
5 papers.
- 2022On learning Whittle index policy for restless bandits with scalable regretcited by 9
- 2022Whittle Index based Q-Learning for Wireless Edge Caching with Linear Function Approximationcited by 7
- 2022Fast Change Identification in Multi-Play Bandits and its Applications in Wireless Networkscited by 2
- 2024Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networkscited by 0
- 2026Restless Bandits with Individual Penalty Constraints: Near-Optimal Indices and Deep Reinforcement Learningcited by 0
- 2026Restless Bandits with Individual Penalty Constraints: Near-Optimal Indices and Deep Reinforcement Learning5 Apr 2026
- 2024Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks25 Apr 2024
- 2022Fast Change Identification in Multi-Play Bandits and its Applications in Wireless Networks20 May 2022
- 2022Whittle Index based Q-Learning for Wireless Edge Caching with Linear Function Approximation26 Feb 2022
- 2022On learning Whittle index policy for restless bandits with scalable regret7 Feb 2022