Sixth-generation (6G) mobile networks are foreseen to be intelligent, pervasive, and automated systems that support a broad spectrum of emerging applications, such as immersive extended reality, autonomous mobility, and personalized AI agents. These applications demand transformative network management, requiring real-time intelligence, semantic awareness, and user-centric optimization. However, existing network management frameworks, including those based on conventional deep learning, struggle with the non-stationary, heterogeneous nature of 6G environments. Generative Artificial Intelligence (GAI) emerges as a pivotal paradigm to address these limitations with its capabilities in context-aware generation, adaptive reasoning, and dynamic decision-making. This survey pioneers a comprehensive examination of the bidirectional integration between GAI and mobile networking. First, it explores how GAI techniques fundamentally transform network management across critical domains, including channel modeling, transmission optimization, beamforming, routing, network slicing, and resource orchestration. Second, it systematically investigates how mobile networking must evolve in architecture, data management, mobility support, and resource orchestration to effectively deploy, infer, and adapt increasingly complex GAI models under dynamic and constrained conditions. Distinct from existing works, we provide a structured review of GAI developments and their interplay with mobile networking, thoroughly analyze applications and enabling techniques, identify critical technical, architectural, and practical challenges, and propose concrete research directions. Our work bridges the gap between GAI and mobile networking, outlining pathways towards intelligent, adaptive, and collaborative 6G systems.
Li et al. (Tue,) studied this question.
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