  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.
