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.

翻译：下一代（xG）无线网络因其复杂性和动态特性，对传统优化技术的应用提出了重大挑战。生成式人工智能（GAI）凭借其独特优势，成为一种强大的工具。与传统优化技术及其他机器学习方法不同，GAI擅长从真实网络数据中学习，捕捉其复杂细节。这使得能够安全、离线地探索多种配置，并生成多样化的、未见过的场景，从而为xG网络实现主动的、数据驱动的探索与优化。此外，GAI的可扩展性使其非常适合大规模xG网络。本文综述了基于GAI的模型如何为xG无线网络开启优化机遇。我们首先回顾了GAI模型以及xG（例如6G）无线网络的一些主要通信范式。接着，我们深入探讨了如何利用GAI来改进资源分配并提升整体网络性能。此外，我们简要回顾了在xG无线网络中支持GAI应用所需的网络要求。本文进一步讨论了利用GAI进行网络优化的关键挑战与未来研究方向。最后，通过一个案例研究，展示了基于扩散的GAI模型在非地面网络（xG网络的核心技术之一）中，用于负载均衡、载波聚合和回程优化的应用。该案例研究作为一个实际示例，说明了如何结合强化学习与GAI来解决现实世界的网络优化问题。