Generative AI Enabled Matching for 6G Multiple Access
This work addresses complex matching problems in 6G wireless networks, offering a potential solution for improved network performance, though it appears incremental as it builds on existing generative AI methods.
The paper tackles the challenge of real-time and stable matching generation in 6G multiple access networks by proposing a generative AI framework based on diffusion models, which generates more effective matching strategies than decision-based AI approaches for tasks like task allocation.
In wireless networks, applying deep learning models to solve matching problems between different entities has become a mainstream and effective approach. However, the complex network topology in 6G multiple access presents significant challenges for the real-time performance and stability of matching generation. Generative artificial intelligence (GenAI) has demonstrated strong capabilities in graph feature extraction, exploration, and generation, offering potential for graph-structured matching generation. In this paper, we propose a GenAI-enabled matching generation framework to support 6G multiple access. Specifically, we first summarize the classical matching theory, discuss common GenAI models and applications from the perspective of matching generation. Then, we propose a framework based on generative diffusion models (GDMs) that iteratively denoises toward reward maximization to generate a matching strategy that meets specific requirements. Experimental results show that, compared to decision-based AI approaches, our framework can generate more effective matching strategies based on given conditions and predefined rewards, helping to solve complex problems in 6G multiple access, such as task allocation.