Artificial Intelligence Enabled Internet of Things: Network Architecture and Spectrum Access

TLDR

The rapid proliferation of wireless devices has driven the development of the Internet‑of‑Things, envisioned as massive machine‑type communications within 5G and future 6G networks, where artificial intelligence is expected to enable efficient wireless access. This article introduces centralized and distributed frameworks for AI‑enabled IoT networks. It analyzes key challenges such as random access and spectrum sharing, and presents deep reinforcement learning strategies implemented with various neural network architectures to optimize spectrum access and sensing.

Abstract

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.