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- Publisher Website: 10.1109/SPAWC.2019.8815498
- Scopus: eid_2-s2.0-85072318514
- WOS: WOS:000539626100110
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Conference Paper: Wireless Data Acquisition for Edge Learning: Importance-Aware
Title | Wireless Data Acquisition for Edge Learning: Importance-Aware |
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Authors | |
Issue Date | 2021 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7693 |
Citation | 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2019), Cannes; France, 2-5 July 2019. In IEEE Transactions on Wireless Communications, 2021
, v. 20 n. 1, p. 406-420 How to Cite? |
Abstract | By deploying machine learning algorithms at the network edge, edge learning recently emerges as a promising framework to support intelligent mobile services. It effectively leverages the rich data collected by abundant mobile devices, and exploits the proximate edge computing resource for low-latency execution. Edge learning crosses two disciplines, machine learning and wireless communication, and thereby gives rise to many new research issues. In this paper, we address a wireless data acquisition problem, which involves a retransmission decision in each communication round to optimize the data quality-vs-quantity tradeoff. A new retransmission protocol called importance-aware automatic-repeat-request (importance ARQ) is proposed. Unlike classic ARQ focusing merely on reliability, importance ARQ selectively retransmits a data sample based on its uncertainty that can be measured using the model under training. Underpinning the proposed protocol is an elegant communication-learning relation between two corresponding metrics, i.e., signal-to-noise ratio (SNR) and data uncertainty. This new relation facilitates the design of a simple threshold based policy for retransmission decisions. As demonstrated via experiments with real datasets, the proposed method avoids learning performance degradation caused by channel noise while achieving faster convergence than conventional SNR-based ARQ. |
Persistent Identifier | http://hdl.handle.net/10722/295784 |
ISBN | |
ISSN | 2023 Impact Factor: 8.9 2023 SCImago Journal Rankings: 5.371 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | LIU, D | - |
dc.contributor.author | ZHU, G | - |
dc.contributor.author | Zhang, J | - |
dc.contributor.author | Huang, K | - |
dc.date.accessioned | 2021-02-08T08:13:58Z | - |
dc.date.available | 2021-02-08T08:13:58Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2019), Cannes; France, 2-5 July 2019. In IEEE Transactions on Wireless Communications, 2021 , v. 20 n. 1, p. 406-420 | - |
dc.identifier.isbn | 9781538665282 | - |
dc.identifier.issn | 1536-1276 | - |
dc.identifier.uri | http://hdl.handle.net/10722/295784 | - |
dc.description.abstract | By deploying machine learning algorithms at the network edge, edge learning recently emerges as a promising framework to support intelligent mobile services. It effectively leverages the rich data collected by abundant mobile devices, and exploits the proximate edge computing resource for low-latency execution. Edge learning crosses two disciplines, machine learning and wireless communication, and thereby gives rise to many new research issues. In this paper, we address a wireless data acquisition problem, which involves a retransmission decision in each communication round to optimize the data quality-vs-quantity tradeoff. A new retransmission protocol called importance-aware automatic-repeat-request (importance ARQ) is proposed. Unlike classic ARQ focusing merely on reliability, importance ARQ selectively retransmits a data sample based on its uncertainty that can be measured using the model under training. Underpinning the proposed protocol is an elegant communication-learning relation between two corresponding metrics, i.e., signal-to-noise ratio (SNR) and data uncertainty. This new relation facilitates the design of a simple threshold based policy for retransmission decisions. As demonstrated via experiments with real datasets, the proposed method avoids learning performance degradation caused by channel noise while achieving faster convergence than conventional SNR-based ARQ. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7693 | - |
dc.relation.ispartof | IEEE Transactions on Wireless Communications | - |
dc.rights | IEEE 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.title | Wireless Data Acquisition for Edge Learning: Importance-Aware | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Huang, K: huangkb@eee.hku.hk | - |
dc.identifier.authority | Huang, K=rp01875 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/SPAWC.2019.8815498 | - |
dc.identifier.scopus | eid_2-s2.0-85072318514 | - |
dc.identifier.hkuros | 321242 | - |
dc.identifier.volume | 20 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 406 | - |
dc.identifier.epage | 420 | - |
dc.identifier.isi | WOS:000539626100110 | - |
dc.publisher.place | United States | - |