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

TL;DR

This paper surveys how artificial intelligence techniques, including deep learning and reinforcement learning, enhance collaborative spectrum sensing in cognitive wireless networks, emphasizing semantic communication and future challenges.

Contribution

It provides a comprehensive overview of AI-driven methods for spectrum sensing, introducing semantic communication as a novel paradigm for efficient wireless network operation.

Findings

AI techniques improve spectrum sensing accuracy and efficiency.

Semantic communication enables joint communication and computation in wireless networks.

The paper identifies open challenges and future research directions in AI-enabled wireless systems.

Abstract

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, fusion strategies and evaluation metrics. Then, we present an overview of the state-of-the-art research on AI-driven CSS, classified into three categories according to learning paradigms: discriminative deep learning (DL), generative DL models, and deep reinforcement learning (DRL). Building on this, we explore AI-empowered semantic communication (SemCom) as a paradigm-shifting solution for CSS. By extracting and transmitting task-relevant features, SemCom upgrades CSS from a computation-centric approach to a highly efficient joint communication and computation framework. Both single-user and multi-user SemCom scenarios are elaborated in detail. Finally, we discuss limitations, open challenges, and future research directions at the intersection of AI and wireless communication.