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Channel estimation and user activity identification in massive grant-free multiple-access

Channel estimation and user activity identification in massive grant-free multiple-access
Channel estimation and user activity identification in massive grant-free multiple-access
Grant-free multiple-access (GFMA) allows to significantly reduce the overhead of granted multiple-access. However, information detection in GFMA is challenging, as it has to be executed along with the activity detection of user equipments (UEs) and channel estimation. In this paper, we study the channel estimation and propose the UE activity identification (UAI) algorithms for the massive connectivity supporting GFMA (mGFMA) systems. For these purposes, the channel estimation is studied from several aspects by assuming different levels of knowledge to the access point, and based on which five UAI approaches are proposed. We study the performance of channel estimation, the statistics of estimated channels, and the performance of UAI algorithms. Our studies show that the proposed approaches are capable of circumventing some of the shortcomings of the existing techniques designed based on compressive sensing and message passing algorithms. They are robust for operation in the mGFMA systems where the active UEs and the number of them are highly dynamic.
296-316
Zhang, Jiatian
ee0c77aa-6bcd-4e08-8ce1-d5bbcd600840
Yang, Lie-Liang
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
Pan, Peng
d9a8f14f-469f-49f1-9462-b7d6f147410d
Maunder, Robert
d6e67cda-a526-4e8e-929f-9ed765a521cf
Zhang, Jiatian
ee0c77aa-6bcd-4e08-8ce1-d5bbcd600840
Yang, Lie-Liang
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
Pan, Peng
d9a8f14f-469f-49f1-9462-b7d6f147410d
Maunder, Robert
d6e67cda-a526-4e8e-929f-9ed765a521cf

Zhang, Jiatian, Yang, Lie-Liang, Pan, Peng and Maunder, Robert (2020) Channel estimation and user activity identification in massive grant-free multiple-access. IEEE Open Journal of Vehicular Technology, 1, 296-316. (doi:10.1109/OJVT.2020.3020228).

Record type: Article

Abstract

Grant-free multiple-access (GFMA) allows to significantly reduce the overhead of granted multiple-access. However, information detection in GFMA is challenging, as it has to be executed along with the activity detection of user equipments (UEs) and channel estimation. In this paper, we study the channel estimation and propose the UE activity identification (UAI) algorithms for the massive connectivity supporting GFMA (mGFMA) systems. For these purposes, the channel estimation is studied from several aspects by assuming different levels of knowledge to the access point, and based on which five UAI approaches are proposed. We study the performance of channel estimation, the statistics of estimated channels, and the performance of UAI algorithms. Our studies show that the proposed approaches are capable of circumventing some of the shortcomings of the existing techniques designed based on compressive sensing and message passing algorithms. They are robust for operation in the mGFMA systems where the active UEs and the number of them are highly dynamic.

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Accepted/In Press date: 26 August 2020
Published date: 28 August 2020

Identifiers

Local EPrints ID: 445852
URI: http://eprints.soton.ac.uk/id/eprint/445852
PURE UUID: d071d47b-2481-42ea-9a6b-39475fa9827a
ORCID for Lie-Liang Yang: ORCID iD orcid.org/0000-0002-2032-9327

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Date deposited: 12 Jan 2021 17:31
Last modified: 12 Jan 2021 17:31

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Contributors

Author: Jiatian Zhang
Author: Lie-Liang Yang ORCID iD
Author: Peng Pan
Author: Robert Maunder

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