  The 5G network connecting billions of Internet-of-Things (IoT) devices will
make it possible to harvest an enormous amount of real-time mobile data.
Furthermore, the 5G virtualization architecture will enable cloud computing at
the (network) edge. The availability of both rich data and computation power at
the edge has motivated Internet companies to deploy artificial intelligence
(AI) there, creating the hot area of edge-AI. Edge learning, the theme of this
project, concerns training edge-AI models, which endow on IoT devices
intelligence for responding to real-time events. However, the transmission of
high-dimensional data from many edge devices to servers can result in excessive
communication latency, creating a bottleneck for edge learning. Traditional
wireless techniques deigned for only radio access are ineffective in tackling
the challenge. Attempts to overcome the communication bottleneck has led to the
development of a new class of techniques for intelligent radio resource
management (RRM), called data-importance aware RRM. Their designs feature the
interplay of active machine learning and wireless communication. Specifically,
the metrics that measure data importance in active learning (e.g.,
classification uncertainty and data diversity) are applied to RRM for efficient
acquisition of distributed data in wireless networks to train AI models at
servers. This article aims at providing an introduction to the emerging area of
importance-aware RRM. To this end, we will introduce the design principles,
survey recent advancements in the area, discuss some design examples, and
suggest some promising research opportunities.
