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Conference Paper: Learning-based Rate Adaptation for Uplink Massive MIMO with A Cooperative Data-Assisted Detector

TitleLearning-based Rate Adaptation for Uplink Massive MIMO with A Cooperative Data-Assisted Detector
Authors
KeywordsUplink
Channel estimation
Interference
MIMO communication
Detectors
Issue Date2019
PublisherInstitute of Electrical and Electronics Engineers.
Citation
Proceedings of 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, Hawaii, USA, 9-13 December 2019, p. 1-6 How to Cite?
AbstractIn this paper, the uplink adaptation for massive multiple-input-multiple-output (MIMO) networks without the knowledge of user density is considered. Specifically, a novel cooperative uplink transmission and detection scheme is first proposed for massive MIMO networks, where each uplink frame is divided into a number of data blocks with independent coding schemes and the following blocks are decoded based on previously detected data blocks in both service and neighboring cells. The asymptotic signal-to- interference-plus-noise ratio (SINR) of the proposed scheme is then derived, and the distribution of interference power considering the randomness of the users' locations is proved to be Gaussian. By tracking the mean and variance of interference power, an online robust rate adaptation algorithm ensuring a target packet outage probability is proposed for the scenario where the interfering channel and the user density are unknown.
Persistent Identifierhttp://hdl.handle.net/10722/291026
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorZhang, Z-
dc.contributor.authorLi, Y-
dc.contributor.authorWang, R-
dc.contributor.authorChen, Y-
dc.contributor.authorHuang, K-
dc.date.accessioned2020-11-02T05:50:31Z-
dc.date.available2020-11-02T05:50:31Z-
dc.date.issued2019-
dc.identifier.citationProceedings of 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, Hawaii, USA, 9-13 December 2019, p. 1-6-
dc.identifier.isbn9781728109626-
dc.identifier.issn2334-0983-
dc.identifier.urihttp://hdl.handle.net/10722/291026-
dc.description.abstractIn this paper, the uplink adaptation for massive multiple-input-multiple-output (MIMO) networks without the knowledge of user density is considered. Specifically, a novel cooperative uplink transmission and detection scheme is first proposed for massive MIMO networks, where each uplink frame is divided into a number of data blocks with independent coding schemes and the following blocks are decoded based on previously detected data blocks in both service and neighboring cells. The asymptotic signal-to- interference-plus-noise ratio (SINR) of the proposed scheme is then derived, and the distribution of interference power considering the randomness of the users' locations is proved to be Gaussian. By tracking the mean and variance of interference power, an online robust rate adaptation algorithm ensuring a target packet outage probability is proposed for the scenario where the interfering channel and the user density are unknown.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers.-
dc.relation.ispartofIEEE Global Communications Conference (GLOBECOM) Proceedings-
dc.rightsIEEE Global Communications Conference (GLOBECOM) Proceedings. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©2019 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.subjectUplink-
dc.subjectChannel estimation-
dc.subjectInterference-
dc.subjectMIMO communication-
dc.subjectDetectors-
dc.titleLearning-based Rate Adaptation for Uplink Massive MIMO with A Cooperative Data-Assisted Detector-
dc.typeConference_Paper-
dc.identifier.emailHuang, K: huangkb@eee.hku.hk-
dc.identifier.authorityHuang, K=rp01875-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/GLOBECOM38437.2019.9014010-
dc.identifier.scopuseid_2-s2.0-85081966040-
dc.identifier.hkuros318011-
dc.identifier.spage1-
dc.identifier.epage6-
dc.publisher.placeUnited States-

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