  The majority of data-driven wireless research leans heavily on discriminative
AI (DAI) that requires vast real-world datasets. Unlike the DAI, Generative AI
(GenAI) pertains to generative models (GMs) capable of discerning the
underlying data distribution, patterns, and features of the input data. This
makes GenAI a crucial asset in wireless domain wherein real-world data is often
scarce, incomplete, costly to acquire, and hard to model or comprehend. With
these appealing attributes, GenAI can replace or supplement DAI methods in
various capacities. Accordingly, this combined tutorial-survey paper commences
with preliminaries of 6G and wireless intelligence by outlining candidate 6G
applications and services, presenting a taxonomy of state-of-the-art DAI
models, exemplifying prominent DAI use cases, and elucidating the multifaceted
ways through which GenAI enhances DAI. Subsequently, we present a tutorial on
GMs by spotlighting seminal examples such as generative adversarial networks,
variational autoencoders, flow-based GMs, diffusion-based GMs, generative
transformers, large language models, to name a few. Contrary to the prevailing
belief that GenAI is a nascent trend, our exhaustive review of approximately
120 technical papers demonstrates the scope of research across core wireless
research areas, including physical layer design; network optimization,
organization, and management; network traffic analytics; cross-layer network
security; and localization & positioning. Furthermore, we outline the central
role of GMs in pioneering areas of 6G network research, including
semantic/THz/near-field communications, ISAC, extremely large antenna arrays,
digital twins, AI-generated content services, mobile edge computing and edge
AI, adversarial ML, and trustworthy AI. Lastly, we shed light on the
multifarious challenges ahead, suggesting potential strategies and promising
remedies.
