.png){kind=icon}
返回
Generative AI Enabled Matching for 6G Multiple Access
.png){kind=icon}
PRE
AI
DOI：10.1109/mwc.2025.3615555.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Generative artificial intelligence
matching generation
6G multiple access
diffusion model
期刊
IF:
11.5
论文数:
2.8K
被引数:
1.3W
机构
引用论文
.png){kind=icon}
Transformer-Empowered Predictive Beamforming for Rate-Splitting Multiple Access in Non-Terrestrial Networks变压器支持的预测波束成形，用于非地面网络中的速率分裂多址访问
PREAI
分享收藏
Diffusion-Based Reinforcement Learning for Edge-Enabled AI-Generated Content Services基于扩散的强化学习，用于支持边缘的AI生成的内容服务
OAAI
OAAI分享收藏分享收藏Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open ChallengesKhoramnejad, F.; Hossain, E. 用于下一代无线网络优化的生成式AI：基础、最新进展和开放挑战。arXiv 2024, arXiv:2405.17454. [谷歌学术] [交叉引用]
PREAI
分享收藏
查看更多内容