# Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs

arXiv: https://arxiv.org/abs/2203.10472 · HTML: https://mlsanity.com/paper/2203.10472/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 65 references that are in the map.
- First posted on arXiv 20 Mar 2022.

## Builds on (most cited first, 28 of 28)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Federated Learning over Wireless Fading Channels](https://mlsanity.com/paper/1907.09769/) (2019)
- [Federated Learning for Open Banking](https://mlsanity.com/paper/2108.10749/) (2021)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning](https://mlsanity.com/paper/1809.07857/) (2018)
- [Toward a 6G AI-Native Air Interface](https://mlsanity.com/paper/2012.08285/) (2020)
- [FedADC: Accelerated Federated Learning with Drift Control](https://mlsanity.com/paper/2012.09102/) (2020)
- [Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning](https://mlsanity.com/paper/2109.04786/) (2021)
- [Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computing](https://mlsanity.com/paper/2110.07136/) (2021)
- [Federated mmWave Beam Selection Utilizing LIDAR Data](https://mlsanity.com/paper/2102.02802/) (2021)
- [Potential and Pitfalls of Multi-Armed Bandits for Decentralized Spatial Reuse in WLANs](https://mlsanity.com/paper/1805.11083/) (2018)
- [Komondor: a Wireless Network Simulator for Next-Generation High-Density WLANs](https://mlsanity.com/paper/1811.12397/) (2018)
- [IEEE 802.11ax: High-Efficiency WLANs](https://mlsanity.com/paper/1501.01496/) (2015)
- [Collaborative Spatial Reuse in Wireless Networks via Selfish Multi-Armed Bandits](https://mlsanity.com/paper/1710.11403/) (2017)
- [Spatial Reuse in IEEE 802.11ax WLANs](https://mlsanity.com/paper/1907.04141/) (2019)
- [Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks](https://mlsanity.com/paper/2005.08281/) (2020)
- [The Shift to 6G Communications: Vision and Requirements](https://mlsanity.com/paper/2010.07993/) (2020)
- [TXOP sharing with Coordinated Spatial Reuse in Multi-AP Cooperative IEEE 802.11be WLANs](https://mlsanity.com/paper/2112.00515/) (2021)
- [Machine Learning for Performance Prediction of Channel Bonding in Next-Generation IEEE 802.11 WLANs](https://mlsanity.com/paper/2105.14219/) (2021)

## Built on by (most cited first, 2 of 2)

- [Private Collaborative Edge Inference via Over-the-Air Computation](https://mlsanity.com/paper/2407.21151/) (2024)
- [Studying the Role of Synthetic Data for Machine Learning-based Wireless Networks Traffic Forecasting](https://mlsanity.com/paper/2601.07646/) (2026)

## Abstract (arXiv)

As wireless standards evolve, more complex functionalities are introduced to address the increasing requirements in terms of throughput, latency, security, and efficiency. To unleash the potential of such new features, artificial intelligence (AI) and machine learning (ML) are currently being exploited for deriving models and protocols from data, rather than by hand-programming. In this paper, we explore the feasibility of applying ML in next-generation wireless local area networks (WLANs). More specifically, we focus on the IEEE 802.11ax spatial reuse (SR) problem and predict its performance through federated learning (FL) models. The set of FL solutions overviewed in this work is part of the 2021 International Telecommunication Union (ITU) AI for 5G Challenge.