AI-Driven Cognitive Radio Networks for Transforming Industries and Sectors Towards a Smart World

In wireless communication technology, the incorporation of Artificial Intelligence (AI) into Cognitive Radio Networks (CRNs) has become a possible paradigm change. To address spectrum scarcity and inefficiencies in wireless communication networks, the CRN can make judgments for dynamic time–frequency-space resource allocation and adapt dynamically to the radio environment. However, CRNs still face significant challenges with dynamic and real-time scenarios. Because of this, cognitive radio (CR) is usually combined with machine learning and artificial intelligence methods for efficient real-time processing. Furthermore, CRNs have entered a new phase because of the development of AI technologies, which have allowed them to progress from passive spectrum sensing to proactive, intelligent, and self-optimizing systems. However, dynamic and real-time scenarios continue to be significant hurdles in cognitive radio networks. Therefore, Cognitive radio is typically integrated with artificial intelligence and machine learning techniques for effective real-time processing. Additionally, the advent of AI technologies has ushered in a new era for CRNs, enabling them to evolve from passive spectrum sensing to proactive, intelligent, and self-optimizing systems. The revolutionary potential of AI-driven CRNs in guiding sectors and industries towards the realization of a fully smart world is examined in this chapter. AI-driven CRNs provide efficient resource allocation, safe communication, and real-time data analytics. As a result, it boosts sustainability, productivity, and safety in a variety of sectors, including manufacturing, transportation, healthcare, and agriculture. To optimize spectrum utilization and network performance, it explains how machine learning algorithms, such as deep learning, reinforcement learning, neural networks, fuzzy logic, and various AI-based optimization methodologies—empower CRNs to sense, learn, and adapt to dynamic spectrum conditions on their own. The current chapter delves into the obstacles that AI-driven cognitive radio networks face, outlining future research directions and highlighting persistent issues.