Abstract: A lot of hopes have been placed in Machine Learning (ML) as a key enabler of future wireless networks. By taking advantage of the large volumes of data generated by networks, ML is expected to deal with the ever-increasing complexity of networking problems, ranging from cloud-based (centralized) to edge-computing-like approaches (decentralized). Unfortunately, current networking systems are not yet prepared for supporting the ensuing requirements of ML-based applications, especially for enabling procedures related to data collection, processing, and output distribution. This paper addresses the challenges of re-designing the network operation to accommodate the utilization of ML into future Wireless Local Area Networks (WLANs). For that, we propose to adopt the International Telecommunications Union (ITU) unified architecture for future networks. Based on the adoption of this architecture, insights are provided into the major challenges of introducing ML to WLANs. Moreover, we describe a set of use cases in which ML is applied to WLAN-related problems.
The document is available here, and the presentation can be found at https://fwilhelmi.github.io/files/fgml5g_input_176_ml_architecture.pdf.