# Distributed Learning in Markovian Restless Bandits over Interference Graphs for Stable Spectrum Sharing

arXiv: https://arxiv.org/abs/2512.17161 · HTML: https://mlsanity.com/paper/2512.17161/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 49 references that are in the map.
- First posted on arXiv 19 Dec 2025.

## Builds on (most cited first, 13 of 13)

- [On Regret-Optimal Learning in Decentralized Multi-player Multi-armed Bandits](https://mlsanity.com/paper/1505.00553/) (2015)
- [Learning in Restless Multi-Armed Bandits via Adaptive Arm Sequencing Rules](https://mlsanity.com/paper/1906.08120/) (2019)
- [Distributed Learning over Markovian Fading Channels for Stable Spectrum Access](https://mlsanity.com/paper/2101.11292/) (2021)
- [Game of Thrones: Fully Distributed Learning for Multi-Player Bandits](https://mlsanity.com/paper/1810.11162/) (2018)
- [Learning in Restless Bandits under Exogenous Global Markov Process](https://mlsanity.com/paper/2112.09484/) (2021)
- [Distributed Learning Algorithms for Spectrum Sharing in Spatial Random Access Wireless Networks](https://mlsanity.com/paper/1507.05664/) (2015)
- [Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks](https://mlsanity.com/paper/2110.14541/) (2021)
- [Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach](https://mlsanity.com/paper/2303.15544/) (2023)
- [Global Rewards in Restless Multi-Armed Bandits](https://mlsanity.com/paper/2406.00738/) (2024)
- [SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks](https://mlsanity.com/paper/2402.17773/) (2024)
- [Distributed Game Theoretic Optimization and Management of Multichannel ALOHA Networks](https://mlsanity.com/paper/1312.7513/) (2013)
- [A Stable Polygamy Approach to Spectrum Access with Channel Reuse](https://mlsanity.com/paper/2408.12402/) (2024)
- [Approximate Best-Response Dynamics in Random Interference Games](https://mlsanity.com/paper/1706.05081/) (2017)

## Abstract (arXiv)

We study distributed learning for spectrum access and sharing among multiple cognitive communication entities, such as cells, subnetworks, or cognitive radio users (collectively referred to as cells), in communication-constrained wireless networks modeled by interference graphs. Our goal is to achieve a globally stable and interference-aware channel allocation. Stability is defined through a generalized Gale-Shapley multi-to-one matching, a well-established solution concept in wireless resource allocation. We consider wireless networks where L cells share S orthogonal channels and cannot simultaneously use the same channel as their neighbors. Each channel evolves as an unknown restless Markov process with cell-dependent rewards, making this the first work to establish global Gale-Shapley stability for channel allocation in a stochastic, temporally varying restless environment. To address this challenge, we develop SMILE (Stable Multi-matching with Interference-aware LEarning), a communication-efficient distributed learning algorithm that integrates restless bandit learning with graph-constrained coordination. SMILE enables cells to distributedly balance exploration of unknown channels with exploitation of learned information. We prove that SMILE converges to the optimal stable allocation and achieves logarithmic regret relative to a genie with full knowledge of expected utilities. Simulations validate the theoretical guarantees and demonstrate SMILE's robustness, scalability, and efficiency across diverse spectrum-sharing scenarios.