Collaborative Spectrum Sensing in Cognitive and Intelligent Wireless Networks: An Artificial Intelligence Perspective

Artificial intelligence (AI) has become a key enabler for next-generation wireless communication systems, offering powerful tools to cope with the increasing complexity, dynamics, and heterogeneity of modern wireless environments. To illustrate the role and impact of AI in wireless communications, this paper takes collaborative spectrum sensing (CSS) in cognitive and intelligent wireless networks as a representative application and surveys recent advances from an AI perspective. We first introduce the fundamentals of CSS, including the general framework, classical detector design, and fusion strategies. Then, we present an overview of the state-of-the-art research on AI-driven CSS, classified into three categories: discriminative deep learning (DL) models, generative DL models, and deep reinforcement learning (DRL). Furthermore, we explore semantic communication (SemCom) as a promising solution for CSS, in which task-oriented representations are exchanged to reduce reporting overhead while preserving decision-critical information. Finally, we discuss limitations, open challenges, and future research directions at the intersection of AI and wireless communication.