arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2504.03499cs.NIcs.LG
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通信网络的乐观学习

Optimistic Learning for Communication Networks

Delft University of Technology(代尔夫特理工大学);Trinity College Dublin(都柏林圣三一学院)

George Iosifidis, Naram Mhaisen, Douglas J. Leith

AI总结:

该教程提出将乐观学习(OpL)作为现代通信网络资源管理的决策引擎,结合离线深度学习的高性能与在线学习的稳健性,并展示了其在缓存、边缘计算等场景中的应用及未来挑战。

摘要:

基于AI/ML的工具处于通信网络资源管理解决方案的前沿。特别是,当有代表性的训练数据可用于构建离线准确模型时,深度学习在促进快速且高性能的决策方面非常有效。相反,在线学习解决方案不需要训练,并允许基于运行时观察进行自适应决策,但往往过于保守。这篇广泛的教程提出将乐观学习(OpL)作为现代通信系统中资源管理框架的决策引擎。如果设计得当,此类解决方案可以实现快速且高性能的决策——与离线训练的模型相当——同时保留各自在线学习方法的稳健性和性能保证。我们介绍了OpL的基本概念、算法和结果,讨论了该理论的根源,并提出了定义和实现乐观性的不同方法。我们接着展示OpL如何增强通信网络中几个关键问题的资源管理,例如去中心化O-RAN平台中的缓存、边缘计算、网络切片和工作负载分配。最后,我们讨论了必须解决的开放挑战,以释放这种新资源管理方法的全部潜力。

英文摘要:

AI/ML-based tools are at the forefront of resource management solutions for communication networks. Deep learning, in particular, is highly effective in facilitating fast and high-performing decision-making whenever representative training data is available to build offline accurate models. Conversely, online learning solutions do not require training and enable adaptive decisions based on runtime observations, alas are often overly conservative. This extensive tutorial proposes the use of optimistic learning (OpL) as a decision engine for resource management frameworks in modern communication systems. When properly designed, such solutions can achieve fast and high-performing decisions -- comparable to offline-trained models -- while preserving the robustness and performance guarantees of the respective online learning approaches. We introduce the fundamental concepts, algorithms and results of OpL, discuss the roots of this theory and present different approaches to defining and achieving optimism. We proceed to showcase how OpL can enhance resource management in communication networks for several key problems such as caching, edge computing, network slicing, and workload assignment in decentralized O-RAN platforms. Finally, we discuss the open challenges that must be addressed to unlock the full potential of this new resource management approach.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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