# A Flexible Machine Learning-Aware Architecture for Future WLANs

arXiv: https://arxiv.org/abs/1910.03510 · HTML: https://mlsanity.com/paper/1910.03510/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2019.

## Built on by (most cited first, 5 of 5)

- [Holistic Network Virtualization and Pervasive Network Intelligence for 6G](https://mlsanity.com/paper/2301.00519/) (2023)
- [AI/ML-based Load Prediction in IEEE 802.11 Enterprise Networks](https://mlsanity.com/paper/2310.07467/) (2023)
- [Analysis and Evaluation of Synchronous and Asynchronous FLchain](https://mlsanity.com/paper/2112.07938/) (2021)
- [Machine Learning for Performance Prediction of Channel Bonding in Next-Generation IEEE 802.11 WLANs](https://mlsanity.com/paper/2105.14219/) (2021)
- [Machine Learning & Wi-Fi: Unveiling the Path Towards AI/ML-Native IEEE 802.11 Networks](https://mlsanity.com/paper/2405.11504/) (2024)

## Abstract (arXiv)

Lots of hopes have been placed on Machine Learning (ML) as a key enabler of future wireless networks. By taking advantage of large volumes of data, ML is expected to deal with the ever-increasing complexity of networking problems. Unfortunately, current networks are not yet prepared to support the ensuing requirements of ML-based applications in terms of data collection, processing, and output distribution. This article points out the architectural requirements that are needed to pervasively include ML as part of future wireless networks operation. Specifically, we look into Wireless Local Area Networks (WLANs), which, due to their nature can be found in multiple forms, ranging from cloud-based to edge-computing-like deployments. In particular, we propose to adopt the International Telecommunications Union (ITU) unified architecture for 5G and beyond. Based on ITU's architecture, we provide insights on the main requirements and the major challenges of introducing ML to the multiple modalities of WLANs. Finally, we showcase the superiority of the architecture through an ML-enabled use case for future networks.