用生成式AI优化6G多接入中的匹配策略，提升实时性与稳定性。
Generative AI Enabled Matching for 6G Multiple Access
- 基于生成扩散模型迭代去噪，以最大化奖励为目标生成匹配策略。
- 实验表明，在任务分配等复杂场景下，性能优于传统决策型AI方法。
- 适合关注6G网络智能调度与生成式人工智能应用的研究者。
在无线网络中，利用深度学习模型解决不同实体间的匹配问题已成为主流且有效的方法。然而，6G多接入中的复杂网络拓扑对匹配生成的实时性和稳定性提出了巨大挑战。生成式人工智能（GenAI）在图特征提取、探索与生成方面展现出强大能力，为图结构匹配生成提供了可能。本文提出一种面向6G多接入的生成式AI增强匹配生成框架。首先，系统梳理经典匹配理论，从匹配生成视角讨论常见GenAI模型及其应用。随后，提出一种基于生成扩散模型（GDMs）的框架，通过迭代去噪逐步逼近奖励最大化，生成满足特定条件的匹配策略。实验结果表明，相较于基于决策的AI方法，该框架能根据给定条件和预设奖励生成更有效的匹配策略，有助于解决6G多接入中的复杂问题，如任务分配。
原文摘要 · Abstract (English)
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.
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