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Conference Paper: Learning Centric Power Allocation for Edge Intelligence

TitleLearning Centric Power Allocation for Edge Intelligence
Authors
Keywordslearning (artificial intelligence)
minimax techniques
pattern classification
radio networks
resource allocation
Issue Date2020
PublisherIEEE. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000104
Citation
ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 7-11 June 2020, p. 1-6 How to Cite?
AbstractWhile machine-type communication (MTC) devices generate massive data, they often cannot process this data due to limited energy and computation power. To this end, edge intelligence has been proposed, which collects distributed data and performs machine learning at the edge. However, this paradigm needs to maximize the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient since they allocate resources merely according to the quality of wireless channels. This paper proposes a learning centric power allocation (LCPA) method, which allocates radio resources based on an empirical classification error model. To get insights into LCPA, an asymptotic optimal solution is derived. The solution shows that the transmit powers are inversely proportional to the channel gain, and scale exponentially with the learning parameters. Experimental results show that the proposed LCPA algorithm significantly outperforms other power allocation algorithms.
Persistent Identifierhttp://hdl.handle.net/10722/289881
ISSN

 

DC FieldValueLanguage
dc.contributor.authorWang, S-
dc.contributor.authorWang, R-
dc.contributor.authorHao, Q-
dc.contributor.authorWu, YC-
dc.contributor.authorPoor, HV-
dc.date.accessioned2020-10-22T08:18:48Z-
dc.date.available2020-10-22T08:18:48Z-
dc.date.issued2020-
dc.identifier.citationICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 7-11 June 2020, p. 1-6-
dc.identifier.issn1550-3607-
dc.identifier.urihttp://hdl.handle.net/10722/289881-
dc.description.abstractWhile machine-type communication (MTC) devices generate massive data, they often cannot process this data due to limited energy and computation power. To this end, edge intelligence has been proposed, which collects distributed data and performs machine learning at the edge. However, this paradigm needs to maximize the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient since they allocate resources merely according to the quality of wireless channels. This paper proposes a learning centric power allocation (LCPA) method, which allocates radio resources based on an empirical classification error model. To get insights into LCPA, an asymptotic optimal solution is derived. The solution shows that the transmit powers are inversely proportional to the channel gain, and scale exponentially with the learning parameters. Experimental results show that the proposed LCPA algorithm significantly outperforms other power allocation algorithms.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000104-
dc.relation.ispartofIEEE International Conference on Communications (ICC)-
dc.rightsIEEE International Conference on Communications (ICC). Copyright © IEEE.-
dc.rights©2020 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.subjectlearning (artificial intelligence)-
dc.subjectminimax techniques-
dc.subjectpattern classification-
dc.subjectradio networks-
dc.subjectresource allocation-
dc.titleLearning Centric Power Allocation for Edge Intelligence-
dc.typeConference_Paper-
dc.identifier.emailWu, YC: ycwu@eee.hku.hk-
dc.identifier.authorityWu, YC=rp00195-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICC40277.2020.9148872-
dc.identifier.scopuseid_2-s2.0-85089427968-
dc.identifier.hkuros316741-
dc.identifier.spage1-
dc.identifier.epage6-
dc.publisher.placeUnited States-

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