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Article: Rate Adaptation for Downlink Massive MIMO Networks and Underlaid D2D Links: A Learning Approach

TitleRate Adaptation for Downlink Massive MIMO Networks and Underlaid D2D Links: A Learning Approach
Authors
KeywordsDevice-to-device communication
Downlink
Interference
MIMO communication
Transmitters
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. 3, p. 1819-1833 How to Cite?
AbstractIn this paper, a novel learning-based rate adaptation mechanism is proposed for a downlink massive multiple-input-multiple-output (MIMO) network with underlaid device-to-device (D2D) links, where the link signal-to-interference-plus-noise ratio (SINR) cannot be accurately predicted before transmission, even its distribution statistics are unknown at the very beginning. Specifically, two coexistence schemes are considered: (1) the D2D links only reuse the downlink subframes; and (2) the D2D receivers also join the uplink channel estimation of the associated cells. For the second scheme, the downlink interference to the D2D receivers is suppressed at the cost of channel training overhead. The geographic distributions of the selected downlink and D2D users in each frame are modeled as two independent stochastic processes with unknown statistics. As a result, the distribution of interference power is unknown to the transmitters. In order to facilitate robust rate allocation, we first derive the asymptotic expressions of downlink and D2D signal-to-interference-plus-noise ratios (SINRs) for sufficiently large antenna number, and show that their distributions can be approximated by Gaussian or exponential random variables. Subsequently, distributive learning algorithms are proposed to evaluate the means and variances of these random variables. This enables the BSs and D2D transmitters to determine the transmission rates under a constraint on packet outage probability.
Persistent Identifierhttp://hdl.handle.net/10722/277226
ISSN
2017 Impact Factor: 5.888
2015 SCImago Journal Rankings: 2.340

 

DC FieldValueLanguage
dc.contributor.authorZHANG, Z-
dc.contributor.authorLi, Y-
dc.contributor.authorWang, R-
dc.contributor.authorHuang, K-
dc.date.accessioned2019-09-20T08:47:01Z-
dc.date.available2019-09-20T08:47:01Z-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Wireless Communications, 2019, v. 18 n. 3, p. 1819-1833-
dc.identifier.issn1536-1276-
dc.identifier.urihttp://hdl.handle.net/10722/277226-
dc.description.abstractIn this paper, a novel learning-based rate adaptation mechanism is proposed for a downlink massive multiple-input-multiple-output (MIMO) network with underlaid device-to-device (D2D) links, where the link signal-to-interference-plus-noise ratio (SINR) cannot be accurately predicted before transmission, even its distribution statistics are unknown at the very beginning. Specifically, two coexistence schemes are considered: (1) the D2D links only reuse the downlink subframes; and (2) the D2D receivers also join the uplink channel estimation of the associated cells. For the second scheme, the downlink interference to the D2D receivers is suppressed at the cost of channel training overhead. The geographic distributions of the selected downlink and D2D users in each frame are modeled as two independent stochastic processes with unknown statistics. As a result, the distribution of interference power is unknown to the transmitters. In order to facilitate robust rate allocation, we first derive the asymptotic expressions of downlink and D2D signal-to-interference-plus-noise ratios (SINRs) for sufficiently large antenna number, and show that their distributions can be approximated by Gaussian or exponential random variables. Subsequently, distributive learning algorithms are proposed to evaluate the means and variances of these random variables. This enables the BSs and D2D transmitters to determine the transmission rates under a constraint on packet outage probability.-
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.subjectDevice-to-device communication-
dc.subjectDownlink-
dc.subjectInterference-
dc.subjectMIMO communication-
dc.subjectTransmitters-
dc.titleRate Adaptation for Downlink Massive MIMO Networks and Underlaid D2D Links: A Learning Approach-
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.2019.2897563-
dc.identifier.scopuseid_2-s2.0-85063061831-
dc.identifier.hkuros305402-
dc.identifier.volume18-
dc.identifier.issue3-
dc.identifier.spage1819-
dc.identifier.epage1833-
dc.publisher.placeUnited States-

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