Generative AI Enabled Matching for 6G Multiple Access

TL;DR

This paper introduces a novel generative AI framework using diffusion models to improve matching strategies in 6G wireless networks, addressing real-time performance and stability challenges.

Contribution

It proposes a diffusion model-based generative framework for matching in 6G networks, enhancing effectiveness over decision-based AI methods.

Findings

Generated matchings outperform decision-based approaches

Framework effectively addresses complex 6G matching problems

Improves real-time stability and performance in network matching

Abstract

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.