A Flexible Machine Learning-Aware Architecture for Future WLANs

TL;DR

This paper proposes an adaptable architecture for integrating machine learning into future WLANs, addressing data handling challenges and demonstrating its effectiveness through a practical use case.

Contribution

It introduces a flexible, ITU-based architecture for ML integration in WLANs, highlighting key requirements and overcoming deployment challenges.

Findings

The architecture supports diverse WLAN deployments from cloud to edge.

It addresses data collection, processing, and distribution challenges.

A use case demonstrates the architecture's effectiveness.

Abstract

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.