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Article: Machine Intelligence at the Edge With Learning Centric Power Allocation

TitleMachine Intelligence at the Edge With Learning Centric Power Allocation
Authors
KeywordsMachine learning
Resource management
Data models
Wireless communication
Computational modeling
Issue Date2020
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7693
Citation
IEEE Transactions on Wireless Communications, 2020, v. 19 n. 11, p. 7293-7308 How to Cite?
AbstractWhile machine-type communication (MTC) devices generate considerable amounts of data, they often cannot process the data due to limited energy and computational power. To empower MTC with intelligence, edge machine learning has been proposed. However, power allocation in this paradigm requires maximizing the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient. To this end, this paper proposes learning centric power allocation (LCPA), which provides a new perspective on radio resource allocation in learning driven scenarios. By employing 1) an empirical classification error model that is supported by learning theory and 2) an uncertainty sampling method that accounts for different distributions at users, LCPA is formulated as a nonconvex nonsmooth optimization problem, and is solved using a majorization minimization (MM) framework. To get deeper insights into LCPA, asymptotic analysis shows that the transmit powers are inversely proportional to the channel gains, and scale exponentially with the learning parameters. This is in contrast to traditional power allocations where quality of wireless channels is the only consideration. Last but not least, a large-scale optimization algorithm termed mirror-prox LCPA is further proposed to enable LCPA in large-scale settings. Extensive numerical results demonstrate that the proposed LCPA algorithms outperform traditional power allocation algorithms, and the large-scale optimization algorithm reduces the computation time by orders of magnitude compared with MM-based LCPA but still achieves competing learning performance.
Descriptionlink_to_subscribed_fulltext
Persistent Identifierhttp://hdl.handle.net/10722/296364
ISSN
2023 Impact Factor: 8.9
2023 SCImago Journal Rankings: 5.371
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, S-
dc.contributor.authorWu, YC-
dc.contributor.authorXia, M-
dc.contributor.authorWang, R-
dc.contributor.authorPoor, HV-
dc.date.accessioned2021-02-22T04:54:15Z-
dc.date.available2021-02-22T04:54:15Z-
dc.date.issued2020-
dc.identifier.citationIEEE Transactions on Wireless Communications, 2020, v. 19 n. 11, p. 7293-7308-
dc.identifier.issn1536-1276-
dc.identifier.urihttp://hdl.handle.net/10722/296364-
dc.descriptionlink_to_subscribed_fulltext-
dc.description.abstractWhile machine-type communication (MTC) devices generate considerable amounts of data, they often cannot process the data due to limited energy and computational power. To empower MTC with intelligence, edge machine learning has been proposed. However, power allocation in this paradigm requires maximizing the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient. To this end, this paper proposes learning centric power allocation (LCPA), which provides a new perspective on radio resource allocation in learning driven scenarios. By employing 1) an empirical classification error model that is supported by learning theory and 2) an uncertainty sampling method that accounts for different distributions at users, LCPA is formulated as a nonconvex nonsmooth optimization problem, and is solved using a majorization minimization (MM) framework. To get deeper insights into LCPA, asymptotic analysis shows that the transmit powers are inversely proportional to the channel gains, and scale exponentially with the learning parameters. This is in contrast to traditional power allocations where quality of wireless channels is the only consideration. Last but not least, a large-scale optimization algorithm termed mirror-prox LCPA is further proposed to enable LCPA in large-scale settings. Extensive numerical results demonstrate that the proposed LCPA algorithms outperform traditional power allocation algorithms, and the large-scale optimization algorithm reduces the computation time by orders of magnitude compared with MM-based LCPA but still achieves competing learning performance.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7693-
dc.relation.ispartofIEEE Transactions on Wireless Communications-
dc.rightsIEEE Transactions on Wireless Communications. 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.subjectMachine learning-
dc.subjectResource management-
dc.subjectData models-
dc.subjectWireless communication-
dc.subjectComputational modeling-
dc.titleMachine Intelligence at the Edge With Learning Centric Power Allocation-
dc.typeArticle-
dc.identifier.emailWu, YC: ycwu@eee.hku.hk-
dc.identifier.authorityWu, YC=rp00195-
dc.identifier.doi10.1109/TWC.2020.3010522-
dc.identifier.scopuseid_2-s2.0-85095552871-
dc.identifier.hkuros321339-
dc.identifier.volume19-
dc.identifier.issue11-
dc.identifier.spage7293-
dc.identifier.epage7308-
dc.identifier.isiWOS:000589218700021-
dc.publisher.placeUnited States-

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