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Deep Learning for Large-Scale MIMO: An Intelligent Wireless Communications Approach.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep Learning for Large-Scale MIMO: An Intelligent Wireless Communications Approach./
Author:	Alrabeiah, Muhammad.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	215 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28651270
ISBN:	9798535547916

Deep Learning for Large-Scale MIMO: An Intelligent Wireless Communications Approach.
Alrabeiah, Muhammad.

Deep Learning for Large-Scale MIMO: An Intelligent Wireless Communications Approach. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 215 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--Arizona State University, 2021.

This item is not available from ProQuest Dissertations & Theses.

Recent years have seen machine learning makes growing presence in several areas inwireless communications, and specifically in large-scale Multiple-Input Multiple-Output(MIMO) systems. This comes as a result of its ability to offer innovative solutions to someof the most daunting problems that haunt current and future large-scale MIMO systems,such as downlink channel-training and sensitivity to line-of-sight (LOS) blockages to nametwo examples. Machine learning, in general, provides wireless systems with data-drivencapabilities, with which they could realize much needed agility for decision-making andadaptability to their surroundings. Bearing the potential of machine learning in mind, thisdissertation takes a close look at what deep learning can bring to the table of large-scaleMIMO systems. It proposes three novel frameworks based on deep learning that tacklechallenges rooted in the need to acquire channel state information. Framework 1, namelydeterministic channel prediction, recognizes that some channels are easier to acquire thanothers (e.g., uplink are easier to acquire than downlink), and, as such, it learns a functionthat predicts some channels (target channels) from others (observed channels). Framework2, namely statistical channel prediction, aims to do the same thing as Framework 1, but ittakes a more statistical approach; it learns a large-scale statistic for target channels (i.e.,per-user channel covariance) from observed channels. Differently from frameworks 1 and2, framework 3, namely vision-aided wireless communications, presents an unorthodoxperspective on dealing with large-scale MIMO challenges specific to high-frequency communications.It relies on the fact that high-frequency communications are reliant on LOSmuch like computer vision. Therefore, it recognizes that parallel and utilizes multimodaldeep learning to address LOS-related challenges, such as downlink beam training and LOSlinkblockages. All three frameworks are studied and discussed using datasets representingvarious large-scale MIMO settings. Overall, they show promising results that cement thevalue of machine learning, especially deep learning, to large-scale MIMO systems.

ISBN: 9798535547916Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

5/6G

Deep Learning for Large-Scale MIMO: An Intelligent Wireless Communications Approach.
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500 $a Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
500 $a Advisor: Alkhateeb, Ahmed.
502 $a Thesis (Ph.D.)--Arizona State University, 2021.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a Recent years have seen machine learning makes growing presence in several areas inwireless communications, and specifically in large-scale Multiple-Input Multiple-Output(MIMO) systems. This comes as a result of its ability to offer innovative solutions to someof the most daunting problems that haunt current and future large-scale MIMO systems,such as downlink channel-training and sensitivity to line-of-sight (LOS) blockages to nametwo examples. Machine learning, in general, provides wireless systems with data-drivencapabilities, with which they could realize much needed agility for decision-making andadaptability to their surroundings. Bearing the potential of machine learning in mind, thisdissertation takes a close look at what deep learning can bring to the table of large-scaleMIMO systems. It proposes three novel frameworks based on deep learning that tacklechallenges rooted in the need to acquire channel state information. Framework 1, namelydeterministic channel prediction, recognizes that some channels are easier to acquire thanothers (e.g., uplink are easier to acquire than downlink), and, as such, it learns a functionthat predicts some channels (target channels) from others (observed channels). Framework2, namely statistical channel prediction, aims to do the same thing as Framework 1, but ittakes a more statistical approach; it learns a large-scale statistic for target channels (i.e.,per-user channel covariance) from observed channels. Differently from frameworks 1 and2, framework 3, namely vision-aided wireless communications, presents an unorthodoxperspective on dealing with large-scale MIMO challenges specific to high-frequency communications.It relies on the fact that high-frequency communications are reliant on LOSmuch like computer vision. Therefore, it recognizes that parallel and utilizes multimodaldeep learning to address LOS-related challenges, such as downlink beam training and LOSlinkblockages. All three frameworks are studied and discussed using datasets representingvarious large-scale MIMO settings. Overall, they show promising results that cement thevalue of machine learning, especially deep learning, to large-scale MIMO systems.
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