In order to effectively provide ultra reliable low latency communications and pervasive connectivity for Internet of Things (IoT) devices, next-generation wireless networks can leverage intelligent, data-driven functions enabled by the integration of machine learning (ML) notions across the wireless core and edge infrastructure. In this context, this paper provides a comprehensive tutorial that overviews how artificial neural networks (ANNs)-based ML algorithms can be employed for solving various wireless networking problems. For this purpose, we first present a detailed overview of a number of key types of ANNs that include recurrent, spiking, and deep neural networks, that are pertinent to wireless networking applications. For each type of ANN, we present the basic architecture as well as specific examples that are particularly important and relevant wireless network design. Such ANN examples include echo state networks, liquid state machine, and long short term memory. Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality applications over wireless networks as well as edge computing and caching. For each individual application, we present the main motivation for using ANNs along with the associated challenges while we also provide a detailed example for a use case scenario and outline future works that can be addressed using ANNs. In a nutshell, this paper constitutes the first holistic tutorial on the development of ANN-based ML techniques tailored to the needs of future wireless networks.

中文翻译：

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基于人工神经网络的无线网络机器学习：教程

为了有效地为物联网 (IoT) 设备提供超可靠的低延迟通信和无处不在的连接，下一代无线网络可以利用通过跨无线核心集成机器学习 (ML) 概念实现的智能数据驱动功能和边缘基础设施。在这种情况下，本文提供了一个综合教程，概述了如何使用基于人工神经网络 (ANN) 的 ML 算法来解决各种无线网络问题。为此，我们首先详细介绍了一些关键类型的 ANN，包括与无线网络应用相关的循环神经网络、尖峰神经网络和深度神经网络。对于每种类型的 ANN，我们介绍了基本架构以及特别重要和相关的无线网络设计的具体示例。此类 ANN 示例包括回声状态网络、液体状态机和长短期记忆。然后，我们深入概述了可以使用 ANN 解决的各种无线通信问题，从使用无人驾驶飞行器的通信到无线网络上的虚拟现实应用以及边缘计算和缓存。对于每个单独的应用程序，我们展示了使用 ANN 的主要动机以及相关的挑战，同时我们还提供了一个用例场景的详细示例，并概述了可以使用 ANN 解决的未来工作。简而言之，