The rich mobile data and edge computing enabled wireless networks motivate to deploy artificial intelligence (AI) at network edge, known as 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.

中文翻译：

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数据重要性感知无线电资源分配：无线通信有助于机器学习

丰富的移动数据和边缘计算使无线网络推动在网络边缘部署人工智能 (AI)，称为边缘 AI，它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，一些数据位更重要。因此，我们可以为更重要的数据分配更多的无线电资源，为不太重要的数据分配更少的无线电资源，从而有效地利用有限的无线电资源。为此，如何定义数据的“或多或少”是关键问题。在本文中，我们提出了两个重要性标准，根据数据对机器学习的影响来区分数据的重要性，一个用于集中式边缘机器学习，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来提高机器学习的性能。进行了大量实验以验证所提出的数据重要性感知无线电资源分配方案的有效性。