论文标题
数据描述无线电资源分配：无线通信有助于机器学习
Data-Importance Aware Radio Resource Allocation: Wireless Communication Helps Machine Learning
论文作者
论文摘要
丰富的移动数据和边缘计算使无线网络激励在网络边缘部署人工智能（AI），该网络边缘（称为\ emph {edge ai}）集成了无线通信和机器学习。在通信中，数据位同样重要，而在机器学习中，一些数据位更为重要。因此，我们可以将更多的无线电资源分配给更重要的数据，并将更少的无线电资源分配给较不重要的数据，以便有效利用有限的无线电资源。为此，如何定义“或多或少重要的数据”是关键问题。在本文中，我们提出了两个重要的标准，以根据数据对机器学习的影响来区分数据的重要性，一个用于集中的边缘机器学习，另一个用于分布式边缘机器学习。然后，提出了相应的无线电资源分配方案，以提高机器学习的性能。进行了广泛的实验，以验证所提出的数据描述无线电资源分配方案的有效性。
The rich mobile data and edge computing enabled wireless networks motivate to deploy artificial intelligence (AI) at network edge, known as \emph{edge AI}, which integrates wireless communication and machine learning. In communication, data bits are equally important, while in machine learning some data bits are more important. Therefore we can allocate more radio resources to the more important data and allocate less radio resources to the less important data, so as to efficiently utilize the limited radio resources. To this end, how to define "more or less important" of data is the key problem. In this article, we propose two importance criteria to differentiate data's importance based on their effects on machine learning, one for centralized edge machine learning and the other for distributed edge machine learning. Then, the corresponding radio resource allocation schemes are proposed to improve performance of machine learning. Extensive experiments are conducted for verifying the effectiveness of the proposed data-importance aware radio resource allocation schemes.
