File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Toward an Intelligent Edge: Wireless Communication Meets Machine Learning

TitleToward an Intelligent Edge: Wireless Communication Meets Machine Learning
Authors
KeywordsServers
Atmospheric modeling
Artificial intelligence
Distributed databases
Wireless communication
Issue Date2020
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at https://www.comsoc.org/publications/magazines/ieee-communications-magazine
Citation
IEEE Communication Magazine, 2020, v. 58 n. 1, p. 19-25 How to Cite?
AbstractThe recent revival of AI is revolutionizing almost every branch of science and technology. Given the ubiquitous smart mobile gadgets and IoT devices, it is expected that a majority of intelligent applications will be deployed at the edge of wireless networks. This trend has generated strong interest in realizing an 'intelligent edge' to support AI-enabled applications at various edge devices. Accordingly, a new research area, called edge learning, has emerged, which crosses and revolutionizes two disciplines: wireless communication and machine learning. A major theme in edge learning is to overcome the limited computing power, as well as limited data, at each edge device. This is accomplished by leveraging the mobile edge computing platform and exploiting the massive data distributed over a large number of edge devices. In such systems, learning from distributed data and communicating between the edge server and devices are two critical and coupled aspects, and their fusion poses many new research challenges. This article advocates a new set of design guidelines for wireless communication in edge learning, collectively called learning- driven communication. Illustrative examples are provided to demonstrate the effectiveness of these design guidelines. Unique research opportunities are identified.
Persistent Identifierhttp://hdl.handle.net/10722/290907
ISSN
2023 Impact Factor: 8.3
2023 SCImago Journal Rankings: 5.631
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhu, G-
dc.contributor.authorLiu, D-
dc.contributor.authorDu, Y-
dc.contributor.authorYou, C-
dc.contributor.authorZhang, J-
dc.contributor.authorHuang, K-
dc.date.accessioned2020-11-02T05:48:47Z-
dc.date.available2020-11-02T05:48:47Z-
dc.date.issued2020-
dc.identifier.citationIEEE Communication Magazine, 2020, v. 58 n. 1, p. 19-25-
dc.identifier.issn0163-6804-
dc.identifier.urihttp://hdl.handle.net/10722/290907-
dc.description.abstractThe recent revival of AI is revolutionizing almost every branch of science and technology. Given the ubiquitous smart mobile gadgets and IoT devices, it is expected that a majority of intelligent applications will be deployed at the edge of wireless networks. This trend has generated strong interest in realizing an 'intelligent edge' to support AI-enabled applications at various edge devices. Accordingly, a new research area, called edge learning, has emerged, which crosses and revolutionizes two disciplines: wireless communication and machine learning. A major theme in edge learning is to overcome the limited computing power, as well as limited data, at each edge device. This is accomplished by leveraging the mobile edge computing platform and exploiting the massive data distributed over a large number of edge devices. In such systems, learning from distributed data and communicating between the edge server and devices are two critical and coupled aspects, and their fusion poses many new research challenges. This article advocates a new set of design guidelines for wireless communication in edge learning, collectively called learning- driven communication. Illustrative examples are provided to demonstrate the effectiveness of these design guidelines. Unique research opportunities are identified.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at https://www.comsoc.org/publications/magazines/ieee-communications-magazine-
dc.relation.ispartofIEEE Communication Magazine-
dc.rightsIEEE Communication Magazine. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectServers-
dc.subjectAtmospheric modeling-
dc.subjectArtificial intelligence-
dc.subjectDistributed databases-
dc.subjectWireless communication-
dc.titleToward an Intelligent Edge: Wireless Communication Meets Machine Learning-
dc.typeArticle-
dc.identifier.emailHuang, K: huangkb@eee.hku.hk-
dc.identifier.authorityHuang, K=rp01875-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/MCOM.001.1900103-
dc.identifier.scopuseid_2-s2.0-85078757648-
dc.identifier.hkuros318043-
dc.identifier.volume58-
dc.identifier.issue1-
dc.identifier.spage19-
dc.identifier.epage25-
dc.identifier.isiWOS:000526802600004-
dc.publisher.placeUnited States-
dc.identifier.issnl0163-6804-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats