.png){kind=icon}
返回
AI-Generated Network Design: A Diffusion Model-Based Learning Approach
.png){kind=icon}
OA
AI
DOI：10.1109/MNET.2023.3321538.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The future networks pose intense demands for intelligent and customized designs to cope with the surging network scale, dynamically time-varying environments, diverse user requirements, and complicated manual configuration. However, traditional rule-based solutions heavily rely on human efforts and expertise, while data-driven intelligent algorithms still lack interpretability and generalization. In this paper, we propose the AIGN (AI-Generated Network), a novel intention-driven paradigm for network design, which allows operators to quickly generate a variety of customized network solutions and achieve expert-free problem optimization. Driven by the diffusion model-based learning approach, AIGN has great potential to learn the reward-maximizing trajectories, automatically satisfy multiple constraints, adapt to different objectives and scenarios, or even intelligently create novel designs and mechanisms unseen in existing network environments. Finally, we conduct a use case to demonstrate that AIGN can effectively guide the design of transmit power allocation in digital twin-based access networks.
Keyword:
Optimization
Protocols
Decision making
Heuristic algorithms
Computational modeling
Trajectory
Planning
Generative adversarial networks
Artificial intelligence
Reinforcement learning
Future Networks
Generative Artificial Intelligence
Diffusion Model
Reinforcement Learning
期刊
IF:
6.3
论文数:
2.7K
被引数:
1.1W
机构
查看更多机构
引用论文
Towards a Learning-Based Framework for Self-Driving Design of Networking Protocols.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Obinutuzumab (GA101) More Potently Engages Phagocytic-Lineage Cells Resulting In Enhanced Monocyte and Macrophage Activity When Compared To Rituximab and OfatumumabBlood
BloodIF0
PREAI
分享收藏Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial支持AI的无线网络的单代理和多代理深度强化学习: 教程OAAI
OAAI分享收藏6G Wireless Systems: Vision, Requirements, Challenges, Insights, and OpportunitiesPROCEEDINGS OF THE IEEEOAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏Applications of Deep Reinforcement Learning in Communications and Networking: A Survey深度强化学习在通信和网络中的应用: 综述OAAI
OAAI分享收藏 没有更多内容