# Distributed Learning over Markovian Fading Channels for Stable Spectrum Access

arXiv: https://arxiv.org/abs/2101.11292 · HTML: https://mlsanity.com/paper/2101.11292/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2021.

## Built on by (most cited first, 13 of 13)

- [Federated Learning: A Signal Processing Perspective](https://mlsanity.com/paper/2103.17150/) (2021)
- [A survey on multi-player bandits](https://mlsanity.com/paper/2211.16275/) (2022)
- [Learning in Restless Bandits under Exogenous Global Markov Process](https://mlsanity.com/paper/2112.09484/) (2021)
- [Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks](https://mlsanity.com/paper/2110.14541/) (2021)
- [Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach](https://mlsanity.com/paper/2303.10373/) (2023)
- [Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach](https://mlsanity.com/paper/2303.15544/) (2023)
- [SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks](https://mlsanity.com/paper/2402.17773/) (2024)
- [Restless Multi-Armed Bandits under Exogenous Global Markov Process](https://mlsanity.com/paper/2202.13665/) (2022)
- [Sparse Training for Federated Learning with Regularized Error Correction](https://mlsanity.com/paper/2312.13795/) (2023)
- [DeepOFW: Deep Learning-Driven OFDM-Flexible Waveform Modulation for Peak-to-Average Power Ratio Reduction](https://mlsanity.com/paper/2603.23544/) (2026)
- [A Masked Pruning Approach for Dimensionality Reduction in Communication-Efficient Federated Learning Systems](https://mlsanity.com/paper/2312.03889/) (2023)
- [Control-Optimized Deep Reinforcement Learning for Artificially Intelligent Autonomous Systems](https://mlsanity.com/paper/2507.00268/) (2025)
- [Distributed Learning in Markovian Restless Bandits over Interference Graphs for Stable Spectrum Sharing](https://mlsanity.com/paper/2512.17161/) (2025)

## Abstract (arXiv)

We consider the problem of multi-user spectrum access in wireless networks. The bandwidth is divided into K orthogonal channels, and M users aim to access the spectrum. Each user chooses a single channel for transmission at each time slot. The state of each channel is modeled by a restless unknown Markovian process. Previous studies have analyzed a special case of this setting, in which each channel yields the same expected rate for all users. By contrast, we consider a more general and practical model, where each channel yields a different expected rate for each user. This model adds a significant challenge of how to efficiently learn a channel allocation in a distributed manner to yield a global system-wide objective. We adopt the stable matching utility as the system objective, which is known to yield strong performance in multichannel wireless networks, and develop a novel Distributed Stable Strategy Learning (DSSL) algorithm to achieve the objective. We prove theoretically that DSSL converges to the stable matching allocation, and the regret, defined as the loss in total rate with respect to the stable matching solution, has a logarithmic order with time. Finally, simulation results demonstrate the strong performance of the DSSL algorithm.