Generative and Large AI Models for 6G Wireless Networks: The Optimization Perspective

The transition to sixth-generation (6G) wireless networks is expected to introduce increasingly complex network architectures, disruptive wireless technologies, ultra-high network density, and diverse service requirements, necessitating highly efficient algorithm design for large-scale and non-convex network optimization. However, conventional optimization-based algorithms usually require sophisticated mathematical modeling and exhibit high computational complexity, while classic learning-based algorithms often suffer from poor robustness and generalization, as well as a lack of cross-scenario meta-optimization capabilities. In contrast, given their strong reasoning and contextual understanding abilities, generative and large artificial intelligence (AI) models are emerging as promising technologies to overcome these limitations. In this article, we propose the leveraging of generative and large AI models for scalable and generalizable network optimization, with an emphasis on facilitating information compression, beamforming design, and automated optimization for dynamic wireless networks with limited radio resources. We introduce a diffusion-based generation framework to solve multi-objective optimization problems for efficient information compression and transmission. We also present a large AI model-based framework for solving non-convex continuous optimization problems for beamforming design in both cell-free wireless networks and integrated sensing and communication networks. Finally, we propose an innovative large AI model-based framework that can automatically solve mixed-integer nonlinear programming problems for microservice deployment over satellite networks.