The explosive growth of wireless devices motivates the development of the internet-of-things (IoT), which is capable of interconnecting massive and diverse "things" via wireless communications. This is also called massive machine type communications (mMTC) as a part of the undergoing fifth generation (5G) mobile networks. It is envisioned that more sophisticated devices would be connected to form a hyperconnected world with the aids of the sixth generation (6G) mobile networks. To enable wireless accesses of such IoT networks, artificial intelligence (AI) can play an important role. In this article, the frameworks of centralized and distributed AI-enabled IoT networks are introduced. Key technical challenges, including random access and spectrum sharing (spectrum access and spectrum sensing), are analyzed for different network architectures. Deep reinforcement learning (DRL)-based strategies are introduced and neural networks-based approaches are utilized to efficiently realize the DRL strategies for system procedures such as spectrum access and spectrum sensing. Different types of neural networks that could be used in IoT networks to conduct DRL are also discussed.

中文翻译：

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人工智能赋能物联网：网络架构和频谱接入

无线设备的爆炸式增长推动了物联网 (IoT) 的发展，它能够通过无线通信将大量多样的“事物”互连起来。这也称为大规模机器类型通信 (mMTC)，作为正在进行的第五代 (5G) 移动网络的一部分。预计在第六代 (6G) 移动网络的帮助下，将连接更复杂的设备以形成超连接的世界。为了实现此类物联网网络的无线接入，人工智能 (AI) 可以发挥重要作用。在本文中，将介绍集中式和分布式支持 AI 的物联网网络的框架。关键技术挑战，包括随机接入和频谱共享（频谱接入和频谱感知），针对不同的网络架构进行分析。引入了基于深度强化学习 (DRL) 的策略，并利用基于神经网络的方法来有效地实现频谱接入和频谱感知等系统程序的 DRL 策略。还讨论了可在 IoT 网络中用于执行 DRL 的不同类型的神经网络。