  Next-generation (xG) wireless networks, with their complex and dynamic
nature, present significant challenges to using traditional optimization
techniques. Generative AI (GAI) emerges as a powerful tool due to its unique
strengths. Unlike traditional optimization techniques and other machine
learning methods, GAI excels at learning from real-world network data,
capturing its intricacies. This enables safe, offline exploration of various
configurations and generation of diverse, unseen scenarios, empowering
proactive, data-driven exploration and optimization for xG networks.
Additionally, GAI's scalability makes it ideal for large-scale xG networks.
This paper surveys how GAI-based models unlock optimization opportunities in xG
wireless networks. We begin by providing a review of GAI models and some of the
major communication paradigms of xG (e.g., 6G) wireless networks. We then delve
into exploring how GAI can be used to improve resource allocation and enhance
overall network performance. Additionally, we briefly review the networking
requirements for supporting GAI applications in xG wireless networks. The paper
further discusses the key challenges and future research directions in
leveraging GAI for network optimization. Finally, a case study demonstrates the
application of a diffusion-based GAI model for load balancing, carrier
aggregation, and backhauling optimization in non-terrestrial networks, a core
technology of xG networks. This case study serves as a practical example of how
the combination of reinforcement learning and GAI can be implemented to address
real-world network optimization problems.
