# Distributed Learning for Channel Allocation Over a Shared Spectrum

arXiv: https://arxiv.org/abs/1902.06353 · HTML: https://mlsanity.com/paper/1902.06353/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2019.

## Built on by (most cited first, 8 of 8)

- [Decentralized Task Offloading in Edge Computing: A Multi-User Multi-Armed Bandit Approach](https://mlsanity.com/paper/2112.11818/) (2021)
- [Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guarantees](https://mlsanity.com/paper/2105.04077/) (2021)
- [Distributed Deep Reinforcement Learning for Collaborative Spectrum Sharing](https://mlsanity.com/paper/2104.02059/) (2021)
- [Medium Access Control protocol for Collaborative Spectrum Learning in Wireless Networks](https://mlsanity.com/paper/2111.12581/) (2021)
- [Communication Efficient Distributed Learning over Wireless Channels](https://mlsanity.com/paper/2209.01682/) (2022)
- [Multi-armed Bandit Algorithms on System-on-Chip: Go Frequentist or Bayesian?](https://mlsanity.com/paper/2106.02855/) (2021)
- [Near-Optimal Privacy-Preserving Learning for Max-Min Fair Multi-Agent Bandits](https://mlsanity.com/paper/2306.04498/) (2023)
- [A Context Augmented Multi-Play Multi-Armed Bandit Algorithm for Fast Channel Allocation in Opportunistic Spectrum Access](https://mlsanity.com/paper/2605.25391/) (2026)

## Abstract (arXiv)

Channel allocation is the task of assigning channels to users such that some objective (e.g., sum-rate) is maximized. In centralized networks such as cellular networks, this task is carried by the base station which gathers the channel state information (CSI) from the users and computes the optimal solution. In distributed networks such as ad-hoc and device-to-device (D2D) networks, no base station exists and conveying global CSI between users is costly or simply impractical. When the CSI is time varying and unknown to the users, the users face the challenge of both learning the channel statistics online and converge to a good channel allocation. This introduces a multi-armed bandit (MAB) scenario with multiple decision makers. If two users or more choose the same channel, a collision occurs and they all receive zero reward. We propose a distributed channel allocation algorithm that each user runs and converges to the optimal allocation while achieving an order optimal regret of Oleft(log Tright). The algorithm is based on a carrier sensing multiple access (CSMA) implementation of the distributed auction algorithm. It does not require any exchange of information between users. Users need only to observe a single channel at a time and sense if there is a transmission on that channel, without decoding the transmissions or identifying the transmitting users. We demonstrate the performance of our algorithm using simulated LTE and 5G channels.