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Article: Reduced-Dimension Design of MIMO Over-the-Air Computing for Data Aggregation in Clustered IoT Networks

TitleReduced-Dimension Design of MIMO Over-the-Air Computing for Data Aggregation in Clustered IoT Networks
Authors
KeywordsMIMO communication
Sensors
Array signal processing
Wireless communication
Wireless sensor networks
Issue Date2019
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, 2019, v. 18 n. 11, p. 5255-5268 How to Cite?
AbstractOne basic operation of Internet-of-Things (IoT) networks is to acquire a function of distributed data collected from sensors over wireless channels, called wireless data aggregation (WDA). In the presence of dense sensors, low-latency WDA poses a design challenge for high-mobility or mission critical IoT applications. A promising solution is a low-latency multi-access scheme, called over-the-air computing (AirComp), that supports simultaneous transmission such that an access point (AP) can estimate and receive a summation-form function of the distributed sensing data by exploiting the waveformsuperposition property of a multi-access channel. In this work, we propose a multiple-input-multiple-output (MIMO) AirComp framework for an IoT network with clustered multi-antenna sensors and an AP with large receive arrays. The framework supports low-complexity and low-latency AirComp of a vectorvalued function. The contributions of this work are two-fold. Define the AirComp error as the error in the functional value received at AP due to channel noise. First, under the criterion of minimum error, the optimal receive beamformer aFigures/t the AP, called decomposed aggregation beamformer (DAB), is shown to have a decomposed architecture: the inner component focuses on channel-dimension reduction and the outer component focuses on joint equalization of the resultant low-dimensional small-scale fading channels. In addition, an algorithm is designed to adjust the ranks of individual components of the DAB for a further performance improvement. Second, to provision DAB with the required channel state information (CSI), a low-latency channel feedback scheme is proposed by intelligently leveraging the AirComp principle to support simultaneous channel feedback by sensors. The proposed framework is shown by simulation to substantially reduce AirComp error compared with the existing design without considering channel structures.
Persistent Identifierhttp://hdl.handle.net/10722/277228
ISSN
2019 Impact Factor: 6.779
2015 SCImago Journal Rankings: 2.340

 

DC FieldValueLanguage
dc.contributor.authorWEN, D-
dc.contributor.authorZHU, G-
dc.contributor.authorHuang, K-
dc.date.accessioned2019-09-20T08:47:03Z-
dc.date.available2019-09-20T08:47:03Z-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Wireless Communications, 2019, v. 18 n. 11, p. 5255-5268-
dc.identifier.issn1536-1276-
dc.identifier.urihttp://hdl.handle.net/10722/277228-
dc.description.abstractOne basic operation of Internet-of-Things (IoT) networks is to acquire a function of distributed data collected from sensors over wireless channels, called wireless data aggregation (WDA). In the presence of dense sensors, low-latency WDA poses a design challenge for high-mobility or mission critical IoT applications. A promising solution is a low-latency multi-access scheme, called over-the-air computing (AirComp), that supports simultaneous transmission such that an access point (AP) can estimate and receive a summation-form function of the distributed sensing data by exploiting the waveformsuperposition property of a multi-access channel. In this work, we propose a multiple-input-multiple-output (MIMO) AirComp framework for an IoT network with clustered multi-antenna sensors and an AP with large receive arrays. The framework supports low-complexity and low-latency AirComp of a vectorvalued function. The contributions of this work are two-fold. Define the AirComp error as the error in the functional value received at AP due to channel noise. First, under the criterion of minimum error, the optimal receive beamformer aFigures/t the AP, called decomposed aggregation beamformer (DAB), is shown to have a decomposed architecture: the inner component focuses on channel-dimension reduction and the outer component focuses on joint equalization of the resultant low-dimensional small-scale fading channels. In addition, an algorithm is designed to adjust the ranks of individual components of the DAB for a further performance improvement. Second, to provision DAB with the required channel state information (CSI), a low-latency channel feedback scheme is proposed by intelligently leveraging the AirComp principle to support simultaneous channel feedback by sensors. The proposed framework is shown by simulation to substantially reduce AirComp error compared with the existing design without considering channel structures.-
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.subjectMIMO communication-
dc.subjectSensors-
dc.subjectArray signal processing-
dc.subjectWireless communication-
dc.subjectWireless sensor networks-
dc.titleReduced-Dimension Design of MIMO Over-the-Air Computing for Data Aggregation in Clustered IoT Networks-
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/TWC.2019.2934956-
dc.identifier.scopuseid_2-s2.0-85077322015-
dc.identifier.hkuros305404-
dc.identifier.volume18-
dc.identifier.issue11-
dc.identifier.spage5255-
dc.identifier.epage5268-
dc.publisher.placeUnited States-

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