The University of Southampton
University of Southampton Institutional Repository

Estimation of broadband multiuser millimeter-wave massive MIMO-OFDM channels by exploiting their sparse structure

Estimation of broadband multiuser millimeter-wave massive MIMO-OFDM channels by exploiting their sparse structure
Estimation of broadband multiuser millimeter-wave massive MIMO-OFDM channels by exploiting their sparse structure
In millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, acquiring accurate channel state information is essential for efficient beamforming (BF) and multiuser interference cancellation, which is a challenging task since a low signal-to-noise ratio is encountered before BF in large antenna arrays. The mmWave channel exhibits a 3-D clustered structure in the virtual angle of arrival (AOA), angle of departure (AOD) and delay domain that is imposed by the effect of power leakage, angular spread and cluster duration. We extend the approximate message passing (AMP) with nearest neighbor pattern learning algorithm for improving the attainable channel estimation performance, which adaptively learns and exploits the clustered structure in the 3-D virtual AOA-AOD-delay domain. The proposed method is capable of approaching the performance bound described by the state evolution based on vector AMP framework, and our simulation results verify its superiority in mmWave systems associated with a broad bandwidth.
1536-1276
Lin, Xingcong
78a6f6a0-32a8-40de-a0fa-29f8d7316ee3
Wu, Sheng
aeb3f610-d00f-4c44-a418-abc97afc9e65
Jiang, Chunxiao
16bad068-43b1-41d4-9f6b-211acdb1ae52
Kuang, Lingling
3da08ed7-3d91-4bd3-8b24-91699e3bbd7b
Yan, Jian
c886a796-3a8f-4c5f-803b-f4f96bf36637
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Lin, Xingcong
78a6f6a0-32a8-40de-a0fa-29f8d7316ee3
Wu, Sheng
aeb3f610-d00f-4c44-a418-abc97afc9e65
Jiang, Chunxiao
16bad068-43b1-41d4-9f6b-211acdb1ae52
Kuang, Lingling
3da08ed7-3d91-4bd3-8b24-91699e3bbd7b
Yan, Jian
c886a796-3a8f-4c5f-803b-f4f96bf36637
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Lin, Xingcong, Wu, Sheng, Jiang, Chunxiao, Kuang, Lingling, Yan, Jian and Hanzo, Lajos (2018) Estimation of broadband multiuser millimeter-wave massive MIMO-OFDM channels by exploiting their sparse structure. IEEE Transactions on Wireless Communications. (doi:10.1109/TWC.2018.2818142).

Record type: Article

Abstract

In millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, acquiring accurate channel state information is essential for efficient beamforming (BF) and multiuser interference cancellation, which is a challenging task since a low signal-to-noise ratio is encountered before BF in large antenna arrays. The mmWave channel exhibits a 3-D clustered structure in the virtual angle of arrival (AOA), angle of departure (AOD) and delay domain that is imposed by the effect of power leakage, angular spread and cluster duration. We extend the approximate message passing (AMP) with nearest neighbor pattern learning algorithm for improving the attainable channel estimation performance, which adaptively learns and exploits the clustered structure in the 3-D virtual AOA-AOD-delay domain. The proposed method is capable of approaching the performance bound described by the state evolution based on vector AMP framework, and our simulation results verify its superiority in mmWave systems associated with a broad bandwidth.

Text
final - Accepted Manuscript
Download (1MB)

More information

Accepted/In Press date: 9 March 2018
e-pub ahead of print date: 29 March 2018

Identifiers

Local EPrints ID: 419207
URI: http://eprints.soton.ac.uk/id/eprint/419207
ISSN: 1536-1276
PURE UUID: 5ca7fbb7-517f-4291-a12d-a1c7d7030a3d
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 09 Apr 2018 16:30
Last modified: 18 Mar 2024 02:35

Export record

Altmetrics

Contributors

Author: Xingcong Lin
Author: Sheng Wu
Author: Chunxiao Jiang
Author: Lingling Kuang
Author: Jian Yan
Author: Lajos Hanzo ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×