Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart devices connected to the Internet. In the wake of disruptive IoT with a huge amount and variety of data, Machine Learning (ML) and Deep Learning (DL) mechanisms will play a pivotal role to bring intelligence to the IoT networks. Among other aspects, ML and DL can play an essential role in addressing the challenges of resource management in large-scale IoT networks. In this article, we conduct a systematic and in-depth survey of the ML- and DL-based resource management mechanisms in cellular wireless and IoT networks. We start with the challenges of resource management in cellular IoT and low-power IoT networks, review the traditional resource management mechanisms for IoT networks, and motivate the use of ML and DL techniques for resource management in these networks. Then, we provide a comprehensive survey of the existing ML- and DL-based resource management techniques in wireless IoT networks and the techniques specifically designed for HetNets, MIMO and D2D communications, and NOMA networks. To this end, we also identify the future research directions in using ML and DL for resource allocation and management in IoT networks.

中文翻译：

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用于蜂窝和物联网网络资源管理的机器学习：潜力、当前解决方案和开放挑战

物联网（IoT）是指通过连接到互联网的智能设备形成的大规模异构网络。随着具有海量数据的颠覆性物联网出现，机器学习 (ML) 和深度学习 (DL) 机制将在为物联网网络带来智能方面发挥关键作用。在其他方面，ML 和 DL 可以在解决大规模物联网网络中的资源管理挑战方面发挥重要作用。在本文中，我们对蜂窝无线和物联网网络中基于 ML 和 DL 的资源管理机制进行了系统而深入的调查。我们从蜂窝物联网和低功耗物联网网络中资源管理的挑战开始，回顾物联网网络的传统资源管理机制，并鼓励在这些网络中使用 ML 和 DL 技术进行资源管理。然后，我们对无线物联网网络中现有的基于 ML 和 DL 的资源管理技术以及专为 HetNet、MIMO 和 D2D 通信以及 NOMA 网络设计的技术进行了全面调查。为此，我们还确定了在物联网网络中使用 ML 和 DL 进行资源分配和管理的未来研究方向。