Advancing 6G: Survey for Explainable AI on Communications and Network Slicing

Abstract

The unprecedented advancement of Artificial Intelligence (AI) has positioned Explainable AI (XAI) as a critical enabler in addressing the complexities of next-generation wireless communications. With the evolution of the 6G networks, characterized by ultra-low latency, massive data rates, and intricate network structures, the need for transparency, interpretability, and fairness in AI-driven decision-making has become more urgent than ever. This survey provides a comprehensive review of the current state and future potential of XAI in communications, with a focus on network slicing, a fundamental technology for resource management in 6G. By systematically categorizing XAI methodologies–ranging from modelagnostic to model-specific approaches, and from pre-model to post-model strategies–this paper identifies their unique advantages, limitations, and applications in wireless communications. Moreover, the survey emphasizes the role of XAI in network slicing for vehicular network, highlighting its ability to enhance transparency and reliability in scenarios requiring real-time decision-making and high-stakes operational environments. Real-world use cases are examined to illustrate how XAI-driven systems can improve resource allocation, facilitate fault diagnosis, and meet regulatory requirements for ethical AI deployment. By addressing the inherent challenges of applying XAI in complex, dynamic networks, this survey offers critical insights into the convergence of XAI and 6G technologies. Future research directions, including scalability, real-time applicability, and interdisciplinary integration, are discussed, establishing a foundation for advancing transparent and trustworthy AI in 6G communications systems.