Evidence map›Paper›PMID 35875642›Full record

ArticlePeerJ. Computer science2022

Performance evaluation of frequency division duplex (FDD) massive multiple input multiple output (MIMO) under different correlation models.

Alaa M Abdul-Hadi, Marwah Abdulrazzaq Naser, Muntadher Alsabah, Sadiq H Abdulhussain, Basheera M Mahmmod

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Article in PeerJ. Computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Alaa M Abdul-HadiDepartment of Computer Engineering, University of Baghdad, Al-Jadriya, Baghdad, Iraq.
Marwah Abdulrazzaq NaserDepartment of Architectural Engineering, University of Baghdad, Al-Jadriya, Baghdad, Iraq.
Muntadher AlsabahDepartment of Electronic and Electrical Engineering, University of Sheffield, Sheffield, United Kingdom.
Sadiq H AbdulhussainDepartment of Computer Engineering, University of Baghdad, Al-Jadriya, Baghdad, Iraq.
Basheera M MahmmodDepartment of Computer Engineering, University of Baghdad, Al-Jadriya, Baghdad, Iraq.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Massive multiple-input multiple-output (massive-MIMO) is considered as the key technology to meet the huge demands of data rates in the future wireless communications networks. However, for massive-MIMO systems to realize their maximum potential gain, sufficiently accurate downlink (DL) channel state information (CSI) with low overhead to meet the short coherence time (CT) is required. Therefore, this article aims to overcome the technical challenge of DL CSI estimation in a frequency-division-duplex (FDD) massive-MIMO with short CT considering five different physical correlation models. To this end, the statistical structure of the massive-MIMO channel, which is captured by the physical correlation is exploited to find sufficiently accurate DL CSI estimation. Specifically, to reduce the DL CSI estimation overhead, the training sequence is designed based on the eigenvectors of the transmit correlation matrix. To this end, the achievable sum rate (ASR) maximization and the mean square error (MSE) of CSI estimation with short CT are investigated using the proposed training sequence design. Furthermore, this article examines the effect of channel hardening in an FDD massive-MIMO system. The results demonstrate that in high correlation scenarios, a large loss in channel hardening is obtained. The results reveal that increasing the correlation level reduces the MSE but does not increase the ASR. However, exploiting the spatial correction structure is still very essential for the FDD massive-MIMO systems under limited CT. This finding holds for all the physical correlation models considered.

Indexed as

Correlation modelFrequency-division-duplexMassive-MIMO

Identifiers

PMID35875642
PMCPMC9299273

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.