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 Telecommunication 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.

中文翻译：

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面向未来 WLAN 的灵活机器学习感知架构

人们寄希望于机器学习 (ML) 作为未来无线网络的关键推动因素。通过利用大量数据，机器学习有望处理日益复杂的网络问题。不幸的是，当前的网络尚未准备好支持基于 ML 的应用程序在数据收集、处理和输出分发方面的后续需求。本文指出了将 ML 作为未来无线网络运营的一部分普遍包含的架构要求。具体来说，我们研究了无线局域网 (WLAN)，由于其性质，它可以以多种形式存在，从基于云的部署到类似边缘计算的部署。特别是，我们建议为 5G 及以后采用国际电信联盟 (ITU) 统一架构。基于 ITU 的架构，我们提供了有关将 ML 引入多种 WLAN 模式的主要要求和主要挑战的见解。最后，我们通过支持 ML 的未来网络用例展示了该架构的优越性。