Spectrum Intelligent Radio: Technology, Development, and Future Trends The advent of Industry 4.0 with massive connectivity places significant strains on the current spectrum resources, and challenges the industry and regulators to respond promptly with new disruptive spectrum management strategies. The envisioned spectrum intelligent radio has long been promised to unlock the full potential of spectrum resource. However, the current radio development, with certain elements of intelligence, is nowhere near showing an agile response to the complex radio environments. Following the line of intelligence, we propose to classify spectrum intelligent radio into three streams: classical signal processing, machine learning (ML), and contextual adaptation. We focus on the ML approach, and propose a new intelligent radio architecture with three hierarchical forms: perception, understanding, and reasoning. For each form, we propose some preliminary methods. The proposed perception method achieves fully blind multilevel spectrum sensing. The understanding method accurately predicts the primary usersu0027 coverage across a large area, and the reasoning method performs nearoptimal idle channel selection. Opportunities, challenges, and future visions are also discussed for the realization of a fully intelligent radio.

Tutorial Machine Learning for AIDriven Wireless Networks: Challenges and Opportunities The goal of this tutorial is to provide one of the first holistic tutorials on the topic of machine learning for wireless network design. In particular, we will first provide a comprehensive treatment of the fundamentals of machine learning and artificial neural networks, which are one of the most important pillars of machine learning. After providing a substantial introduction to the basics of machine learning, we introduce a classification of the various types of neural networks that include feedforward neural networks, recurrent neural networks, spiking neural networks, and deep neural networks. For each type, we provide an introduction on their basic components, their training processes, and their use cases with specific example neural networks. Then, we overview a broad range of wireless applications that can make use of neural network designs. This range of applications includes spectrum management, multiple radio access technology cellular networks, wireless virtual reality, mobile edge computing and caching, dronebased communications, the Internet of Things, and vehicular networks. For each application, we first outline the main rationale for applying machine learning while pinpointing illustrative scenarios. Then, we overview the challenges and opportunities brought forward by the use of neural networks in the specific wireless application. We complement this overview with a detailed example drawn from the stateoftheart. Finally, we conclude by shedding light on the potential future works within each specific area and within the overall area of AI for wireless networks.