# Medium Access Control protocol for Collaborative Spectrum Learning in Wireless Networks

arXiv: https://arxiv.org/abs/2111.12581 · HTML: https://mlsanity.com/paper/2111.12581/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 25 Oct 2021.

## Builds on (most cited first, 15 of 15)

- [Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks](https://mlsanity.com/paper/1712.00162/) (2017)
- [A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players](https://mlsanity.com/paper/1902.01239/) (2019)
- [On Regret-Optimal Learning in Decentralized Multi-player Multi-armed Bandits](https://mlsanity.com/paper/1505.00553/) (2015)
- [Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits](https://mlsanity.com/paper/1907.03346/) (2019)
- [My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits](https://mlsanity.com/paper/2002.09808/) (2020)
- [Game of Thrones: Fully Distributed Learning for Multi-Player Bandits](https://mlsanity.com/paper/1810.11162/) (2018)
- [Multi-Player Bandits: The Adversarial Case](https://mlsanity.com/paper/1902.08036/) (2019)
- [Multi-user Communication Networks: A Coordinated Multi-armed Bandit Approach](https://mlsanity.com/paper/1808.04875/) (2018)
- [Distributed Learning for Channel Allocation Over a Shared Spectrum](https://mlsanity.com/paper/1902.06353/) (2019)
- [Resource Allocation in NOMA-based Self-Organizing Networks using Stochastic Multi-Armed Bandits](https://mlsanity.com/paper/2101.06340/) (2021)
- [Distributed Game Theoretic Optimization and Management of Multichannel ALOHA Networks](https://mlsanity.com/paper/1312.7513/) (2013)
- [Decentralized Learning for Channel Allocation in IoT Networks over Unlicensed Bandwidth as a Contextual Multi-player Multi-armed Bandit Game](https://mlsanity.com/paper/2003.13314/) (2020)
- [Fully distributed optimal channel assignment for open spectrum access](https://mlsanity.com/paper/1312.7685/) (2013)
- [A Survey on Applications of Model-Free Strategy Learning in Cognitive Wireless Networks](https://mlsanity.com/paper/1504.03976/) (2015)
- [Multiplayer Multi-armed Bandits for Optimal Assignment in Heterogeneous Networks](https://mlsanity.com/paper/1901.03868/) (2019)

## Built on by (most cited first, 1 of 1)

- [Communication Efficient Distributed Learning over Wireless Channels](https://mlsanity.com/paper/2209.01682/) (2022)

## Abstract (arXiv)

In recent years there is a growing effort to provide learning algorithms for spectrum collaboration. In this paper we present a medium access control protocol which allows spectrum collaboration with minimal regret and high spectral efficiency in highly loaded networks. We present a fully-distributed algorithm for spectrum collaboration in congested ad-hoc networks. The algorithm jointly solves both the channel allocation and access scheduling problems. We prove that the algorithm has an optimal logarithmic regret. Based on the algorithm we provide a medium access control protocol which allows distributed implementation of the algorithm in ad-hoc networks. The protocol utilizes single-channel opportunistic carrier sensing to carry out a low-complexity distributed auction in time and frequency. We also discuss practical implementation issues such as bounded frame size and speed of convergence. Computer simulations comparing the algorithm to state-of-the-art distributed medium access control protocols show the significant advantage of the proposed scheme.