An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning  

作　　者：Dingzhu Wen Xiaoyang Li Qunsong Zeng Jinke Ren Kaibin Huang 

机构地区：[1]Department of Electrical and Electronic Engineering,the University of Hong Kong,Hong Kong,China [2]College of Information Science and Electronic Engineering,Zhejiang University,Hangzhou 310058,China

出　　处：《Journal of Communications and Information Networks》2019年第4期1-14,共14页通信与信息网络学报（英文）

基　　金：supported by Hong Kong Research Grants Council under the Grants 17208319,17209917 and 17259416。

摘　　要：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.

关 键 词：radio resource management scheduling RETRANSMISSION edge machine learning active learning 

分 类 号：TN9[电子电信—信息与通信工程]